{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Phase 1 Graded Challenge 1\n", "\n", "Graded Challenge ini dibuat guna mengevaluasi pembelajaran pada Hacktiv8 Data Science Fulltime Program khususnya pada konsep Regression." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## i. Perkenalan\n", "\n", "Nama : Raden Mas Xyla Ramadhan\n", "\n", "Batch : 13" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uber and Lyft Dataset Boston, MA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Objective" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mampu memahami konsep regression dengan Linear Regression.\n", "\n", "Mampu mempersiapkan data untuk digunakan dalam model Linear Regression.\n", "\n", "Mampu mengimplementasikan Linear Regression untuk membuat prediksi." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ii. Import Libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#import library python yang dipakai\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.preprocessing import OrdinalEncoder\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## iii. Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idtimestamphourdaymonthdatetimetimezonesourcedestinationcab_type...precipIntensityMaxuvIndexTimetemperatureMintemperatureMinTimetemperatureMaxtemperatureMaxTimeapparentTemperatureMinapparentTemperatureMinTimeapparentTemperatureMaxapparentTemperatureMaxTime
0424553bb-7174-41ea-aeb4-fe06d4f4b9d71.544953e+09916122018-12-16 09:30:07America/New_YorkHaymarket SquareNorth StationLyft...0.1276154497960039.89154501200043.68154496880033.73154501200038.071544958000
14bd23055-6827-41c6-b23b-3c491f24e74d1.543284e+09227112018-11-27 02:00:23America/New_YorkHaymarket SquareNorth StationLyft...0.1300154325160040.49154323360047.30154325160036.20154329120043.921543251600
2981a3613-77af-4620-a42a-0c0866077d1e1.543367e+09128112018-11-28 01:00:22America/New_YorkHaymarket SquareNorth StationLyft...0.1064154333800035.36154337760047.55154332000031.04154337760044.121543320000
3c2d88af2-d278-4bfd-a8d0-29ca77cc55121.543554e+09430112018-11-30 04:53:02America/New_YorkHaymarket SquareNorth StationLyft...0.0000154350720034.67154355040045.03154351080030.30154355040038.531543510800
4e0126e1f-8ca9-4f2e-82b3-50505a09db9a1.543463e+09329112018-11-29 03:49:20America/New_YorkHaymarket SquareNorth StationLyft...0.0001154342080033.10154340280042.18154342080029.11154339200035.751543420800
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5 rows × 57 columns

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" ], "text/plain": [ " id timestamp hour day month \\\n", "0 424553bb-7174-41ea-aeb4-fe06d4f4b9d7 1.544953e+09 9 16 12 \n", "1 4bd23055-6827-41c6-b23b-3c491f24e74d 1.543284e+09 2 27 11 \n", "2 981a3613-77af-4620-a42a-0c0866077d1e 1.543367e+09 1 28 11 \n", "3 c2d88af2-d278-4bfd-a8d0-29ca77cc5512 1.543554e+09 4 30 11 \n", "4 e0126e1f-8ca9-4f2e-82b3-50505a09db9a 1.543463e+09 3 29 11 \n", "\n", " datetime timezone source destination \\\n", "0 2018-12-16 09:30:07 America/New_York Haymarket Square North Station \n", "1 2018-11-27 02:00:23 America/New_York Haymarket Square North Station \n", "2 2018-11-28 01:00:22 America/New_York Haymarket Square North Station \n", "3 2018-11-30 04:53:02 America/New_York Haymarket Square North Station \n", "4 2018-11-29 03:49:20 America/New_York Haymarket Square North Station \n", "\n", " cab_type ... precipIntensityMax uvIndexTime temperatureMin \\\n", "0 Lyft ... 0.1276 1544979600 39.89 \n", "1 Lyft ... 0.1300 1543251600 40.49 \n", "2 Lyft ... 0.1064 1543338000 35.36 \n", "3 Lyft ... 0.0000 1543507200 34.67 \n", "4 Lyft ... 0.0001 1543420800 33.10 \n", "\n", " temperatureMinTime temperatureMax temperatureMaxTime \\\n", "0 1545012000 43.68 1544968800 \n", "1 1543233600 47.30 1543251600 \n", "2 1543377600 47.55 1543320000 \n", "3 1543550400 45.03 1543510800 \n", "4 1543402800 42.18 1543420800 \n", "\n", " apparentTemperatureMin apparentTemperatureMinTime apparentTemperatureMax \\\n", "0 33.73 1545012000 38.07 \n", "1 36.20 1543291200 43.92 \n", "2 31.04 1543377600 44.12 \n", "3 30.30 1543550400 38.53 \n", "4 29.11 1543392000 35.75 \n", "\n", " apparentTemperatureMaxTime \n", "0 1544958000 \n", "1 1543251600 \n", "2 1543320000 \n", "3 1543510800 \n", "4 1543420800 \n", "\n", "[5 rows x 57 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#loading data\n", "data = pd.read_csv('rideshare_kaggle.csv')\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cek Informasi Data " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 693071 entries, 0 to 693070\n", "Data columns (total 57 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 id 693071 non-null object \n", " 1 timestamp 693071 non-null float64\n", " 2 hour 693071 non-null int64 \n", " 3 day 693071 non-null int64 \n", " 4 month 693071 non-null int64 \n", " 5 datetime 693071 non-null object \n", " 6 timezone 693071 non-null object \n", " 7 source 693071 non-null object \n", " 8 destination 693071 non-null object \n", " 9 cab_type 693071 non-null object \n", " 10 product_id 693071 non-null object \n", " 11 name 693071 non-null object \n", " 12 price 637976 non-null float64\n", " 13 distance 693071 non-null float64\n", " 14 surge_multiplier 693071 non-null float64\n", " 15 latitude 693071 non-null float64\n", " 16 longitude 693071 non-null float64\n", " 17 temperature 693071 non-null float64\n", " 18 apparentTemperature 693071 non-null float64\n", " 19 short_summary 693071 non-null object \n", " 20 long_summary 693071 non-null object \n", " 21 precipIntensity 693071 non-null float64\n", " 22 precipProbability 693071 non-null float64\n", " 23 humidity 693071 non-null float64\n", " 24 windSpeed 693071 non-null float64\n", " 25 windGust 693071 non-null float64\n", " 26 windGustTime 693071 non-null int64 \n", " 27 visibility 693071 non-null float64\n", " 28 temperatureHigh 693071 non-null float64\n", " 29 temperatureHighTime 693071 non-null int64 \n", " 30 temperatureLow 693071 non-null float64\n", " 31 temperatureLowTime 693071 non-null int64 \n", " 32 apparentTemperatureHigh 693071 non-null float64\n", " 33 apparentTemperatureHighTime 693071 non-null int64 \n", " 34 apparentTemperatureLow 693071 non-null float64\n", " 35 apparentTemperatureLowTime 693071 non-null int64 \n", " 36 icon 693071 non-null object \n", " 37 dewPoint 693071 non-null float64\n", " 38 pressure 693071 non-null float64\n", " 39 windBearing 693071 non-null int64 \n", " 40 cloudCover 693071 non-null float64\n", " 41 uvIndex 693071 non-null int64 \n", " 42 visibility.1 693071 non-null float64\n", " 43 ozone 693071 non-null float64\n", " 44 sunriseTime 693071 non-null int64 \n", " 45 sunsetTime 693071 non-null int64 \n", " 46 moonPhase 693071 non-null float64\n", " 47 precipIntensityMax 693071 non-null float64\n", " 48 uvIndexTime 693071 non-null int64 \n", " 49 temperatureMin 693071 non-null float64\n", " 50 temperatureMinTime 693071 non-null int64 \n", " 51 temperatureMax 693071 non-null float64\n", " 52 temperatureMaxTime 693071 non-null int64 \n", " 53 apparentTemperatureMin 693071 non-null float64\n", " 54 apparentTemperatureMinTime 693071 non-null int64 \n", " 55 apparentTemperatureMax 693071 non-null float64\n", " 56 apparentTemperatureMaxTime 693071 non-null int64 \n", "dtypes: float64(29), int64(17), object(11)\n", "memory usage: 301.4+ MB\n" ] } ], "source": [ "#cek informasi dataset\n", "data.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "bisa kita liat dari informasi tersebut \n", "\n", "dari 56 tipe data \n", "29 adalah float64, 17 adalah int64 dan 11 adalah object" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "RangeIndex: 693071 entries, 0 to 693070\n", "\n", "Data columns (total 57 columns)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(693071, 57)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cek Duplikasi Data " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.duplicated().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "setelah di cek ternyata tidak ada duplikasi data sama sekali menandakan bahwa setiap data yang di input adalah unique" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cek Deskripsi Data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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timestamphourdaymonthpricedistancesurge_multiplierlatitudelongitudetemperature...precipIntensityMaxuvIndexTimetemperatureMintemperatureMinTimetemperatureMaxtemperatureMaxTimeapparentTemperatureMinapparentTemperatureMinTimeapparentTemperatureMaxapparentTemperatureMaxTime
count6.930710e+05693071.000000693071.000000693071.000000637976.000000693071.000000693071.000000693071.000000693071.000000693071.000000...693071.0000006.930710e+05693071.0000006.930710e+05693071.0000006.930710e+05693071.0000006.930710e+05693071.0000006.930710e+05
mean1.544046e+0911.61913717.79436511.58668416.5451252.1894301.01387042.338172-71.06615139.584388...0.0373741.544044e+0933.4577741.544042e+0945.2613131.544047e+0929.7310021.544048e+0941.9973431.544048e+09
std6.891925e+056.9481149.9822860.4924299.3243591.1389370.0916410.0478400.0203026.726084...0.0552146.912028e+056.4672246.901954e+055.6450466.901353e+057.1104946.871862e+056.9368416.910777e+05
min1.543204e+090.0000001.00000011.0000002.5000000.0200001.00000042.214800-71.10540018.910000...0.0000001.543162e+0915.6300001.543122e+0933.5100001.543154e+0911.8100001.543136e+0928.9500001.543187e+09
25%1.543444e+096.00000013.00000011.0000009.0000001.2800001.00000042.350300-71.08100036.450000...0.0000001.543421e+0930.1700001.543399e+0942.5700001.543439e+0927.7600001.543399e+0936.5700001.543439e+09
50%1.543737e+0912.00000017.00000012.00000013.5000002.1600001.00000042.351900-71.06310040.490000...0.0004001.543770e+0934.2400001.543727e+0944.6800001.543788e+0930.1300001.543745e+0940.9500001.543788e+09
75%1.544828e+0918.00000028.00000012.00000022.5000002.9200001.00000042.364700-71.05420043.580000...0.0916001.544807e+0938.8800001.544789e+0946.9100001.544814e+0935.7100001.544789e+0944.1200001.544818e+09
max1.545161e+0923.00000030.00000012.00000097.5000007.8600003.00000042.366100-71.03300057.220000...0.1459001.545152e+0943.1000001.545192e+0957.8700001.545109e+0940.0500001.545134e+0957.2000001.545109e+09
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8 rows × 46 columns

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" ], "text/plain": [ " timestamp hour day month \\\n", "count 6.930710e+05 693071.000000 693071.000000 693071.000000 \n", "mean 1.544046e+09 11.619137 17.794365 11.586684 \n", "std 6.891925e+05 6.948114 9.982286 0.492429 \n", "min 1.543204e+09 0.000000 1.000000 11.000000 \n", "25% 1.543444e+09 6.000000 13.000000 11.000000 \n", "50% 1.543737e+09 12.000000 17.000000 12.000000 \n", "75% 1.544828e+09 18.000000 28.000000 12.000000 \n", "max 1.545161e+09 23.000000 30.000000 12.000000 \n", "\n", " price distance surge_multiplier latitude \\\n", "count 637976.000000 693071.000000 693071.000000 693071.000000 \n", "mean 16.545125 2.189430 1.013870 42.338172 \n", "std 9.324359 1.138937 0.091641 0.047840 \n", "min 2.500000 0.020000 1.000000 42.214800 \n", "25% 9.000000 1.280000 1.000000 42.350300 \n", "50% 13.500000 2.160000 1.000000 42.351900 \n", "75% 22.500000 2.920000 1.000000 42.364700 \n", "max 97.500000 7.860000 3.000000 42.366100 \n", "\n", " longitude temperature ... precipIntensityMax uvIndexTime \\\n", "count 693071.000000 693071.000000 ... 693071.000000 6.930710e+05 \n", "mean -71.066151 39.584388 ... 0.037374 1.544044e+09 \n", "std 0.020302 6.726084 ... 0.055214 6.912028e+05 \n", "min -71.105400 18.910000 ... 0.000000 1.543162e+09 \n", "25% -71.081000 36.450000 ... 0.000000 1.543421e+09 \n", "50% -71.063100 40.490000 ... 0.000400 1.543770e+09 \n", "75% -71.054200 43.580000 ... 0.091600 1.544807e+09 \n", "max -71.033000 57.220000 ... 0.145900 1.545152e+09 \n", "\n", " temperatureMin temperatureMinTime temperatureMax temperatureMaxTime \\\n", "count 693071.000000 6.930710e+05 693071.000000 6.930710e+05 \n", "mean 33.457774 1.544042e+09 45.261313 1.544047e+09 \n", "std 6.467224 6.901954e+05 5.645046 6.901353e+05 \n", "min 15.630000 1.543122e+09 33.510000 1.543154e+09 \n", "25% 30.170000 1.543399e+09 42.570000 1.543439e+09 \n", "50% 34.240000 1.543727e+09 44.680000 1.543788e+09 \n", "75% 38.880000 1.544789e+09 46.910000 1.544814e+09 \n", "max 43.100000 1.545192e+09 57.870000 1.545109e+09 \n", "\n", " apparentTemperatureMin apparentTemperatureMinTime \\\n", "count 693071.000000 6.930710e+05 \n", "mean 29.731002 1.544048e+09 \n", "std 7.110494 6.871862e+05 \n", "min 11.810000 1.543136e+09 \n", "25% 27.760000 1.543399e+09 \n", "50% 30.130000 1.543745e+09 \n", "75% 35.710000 1.544789e+09 \n", "max 40.050000 1.545134e+09 \n", "\n", " apparentTemperatureMax apparentTemperatureMaxTime \n", "count 693071.000000 6.930710e+05 \n", "mean 41.997343 1.544048e+09 \n", "std 6.936841 6.910777e+05 \n", "min 28.950000 1.543187e+09 \n", "25% 36.570000 1.543439e+09 \n", "50% 40.950000 1.543788e+09 \n", "75% 44.120000 1.544818e+09 \n", "max 57.200000 1.545109e+09 \n", "\n", "[8 rows x 46 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "bisa dilihat dari dataframe bahwa ada 46 data yang merupakan numerical columns\n", "\n", "min price adalah 2.5 dan max price adalah 97.5\n", "\n", "min distance adalah 0.02 max distance adalah 7.86\n", "\n", "min surge multiplier adalah 1.00 dan max surge multiplier adalah 3.00" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cek Missing Value" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0\n", "timestamp 0\n", "hour 0\n", "day 0\n", "month 0\n", "datetime 0\n", "timezone 0\n", "source 0\n", "destination 0\n", "cab_type 0\n", "product_id 0\n", "name 0\n", "price 55095\n", "distance 0\n", "surge_multiplier 0\n", "latitude 0\n", "longitude 0\n", "temperature 0\n", "apparentTemperature 0\n", "short_summary 0\n", "long_summary 0\n", "precipIntensity 0\n", "precipProbability 0\n", "humidity 0\n", "windSpeed 0\n", "windGust 0\n", "windGustTime 0\n", "visibility 0\n", "temperatureHigh 0\n", "temperatureHighTime 0\n", "temperatureLow 0\n", "temperatureLowTime 0\n", "apparentTemperatureHigh 0\n", "apparentTemperatureHighTime 0\n", "apparentTemperatureLow 0\n", "apparentTemperatureLowTime 0\n", "icon 0\n", "dewPoint 0\n", "pressure 0\n", "windBearing 0\n", "cloudCover 0\n", "uvIndex 0\n", "visibility.1 0\n", "ozone 0\n", "sunriseTime 0\n", "sunsetTime 0\n", "moonPhase 0\n", "precipIntensityMax 0\n", "uvIndexTime 0\n", "temperatureMin 0\n", "temperatureMinTime 0\n", "temperatureMax 0\n", "temperatureMaxTime 0\n", "apparentTemperatureMin 0\n", "apparentTemperatureMinTime 0\n", "apparentTemperatureMax 0\n", "apparentTemperatureMaxTime 0\n", "dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.isna().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "setelah kita cek terdapat 55095 missing value dari price dari total 693071 baris " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7.949402009317949" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "55095/693071*100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ada 7,94% missing value kolom price dari total keseluruhan data\n", "\n", "untuk handling missing value dilangkah data preprocessing untuk saat ini hanya cek missing value saja" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## iv. Exploratory Data Analysis (EDA)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Karena terdapat 57 jumlah kolom didalam dataset, maka akan dilakukan eksplorasi data yang berhubungan atau berkorelasi dengan target yang akan diprediksi " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Karena kolom price adalah target yang akan diprediksi oleh karena itu saya akan memfokuskan terhadap korelasi tabel price dengan kolom yang lain \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "terdapat 46 numerical columns dan 11 categorical objects oleh karena itu saya akan pisahkan data nya untuk correlation" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "num_data = data.select_dtypes(include=np.number)\n", "cat_data = data.select_dtypes(include=['object'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlation Between Numerical Columns" ] }, { "cell_type": "code", "execution_count": 10, 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 timestamphourdaymonthpricedistancesurge_multiplierlatitudelongitudetemperatureapparentTemperatureprecipIntensityprecipProbabilityhumiditywindSpeedwindGustwindGustTimevisibilitytemperatureHightemperatureHighTimetemperatureLowtemperatureLowTimeapparentTemperatureHighapparentTemperatureHighTimeapparentTemperatureLowapparentTemperatureLowTimedewPointpressurewindBearingcloudCoveruvIndexvisibility.1ozonesunriseTimesunsetTimemoonPhaseprecipIntensityMaxuvIndexTimetemperatureMintemperatureMinTimetemperatureMaxtemperatureMaxTimeapparentTemperatureMinapparentTemperatureMinTimeapparentTemperatureMaxapparentTemperatureMaxTime
timestamp1.000000-0.028004-0.3412920.7711820.0008080.0035530.0005720.168130-0.136802-0.248748-0.226264-0.179958-0.147596-0.168024-0.0661710.0057060.9988420.158993-0.2281250.999332-0.3668590.999175-0.2153630.999313-0.4019990.999203-0.2654440.533025-0.133526-0.081699-0.0257080.1589930.1965180.9993720.999372-0.846203-0.2117000.999364-0.3069710.998194-0.1863280.999290-0.3783460.998286-0.1577480.999276
hour-0.0280041.0000000.066090-0.0793790.0005610.002280-0.0000770.019493-0.0062950.2187690.198190-0.233349-0.129725-0.2757620.0727780.077308-0.0280470.1741290.005172-0.0278380.015326-0.0267970.005513-0.0279970.016005-0.0269700.004839-0.0673660.0360890.0265250.3302090.1741290.034164-0.027537-0.0275350.0204830.019940-0.0275180.015513-0.0270270.002958-0.0279900.015820-0.0277040.002897-0.027785
day-0.3412920.0660901.000000-0.861306-0.000722-0.0006270.0013890.0068380.078690-0.084116-0.2235450.0916640.030903-0.0933400.4405020.402637-0.3364170.104992-0.283561-0.343118-0.088320-0.333558-0.398183-0.343291-0.192984-0.335504-0.101582-0.4446350.092167-0.041190-0.0118850.1049920.377057-0.341068-0.341012-0.0072600.145568-0.3411980.038076-0.333990-0.304870-0.344532-0.019940-0.335043-0.444445-0.344411
month0.771182-0.079379-0.8613061.0000000.0009130.002298-0.0006280.085791-0.126912-0.0817740.024983-0.154531-0.098031-0.022256-0.334803-0.2704940.7672650.0114930.0683130.772053-0.1387320.7654890.1526510.772163-0.0869980.766822-0.0747940.589609-0.135103-0.016800-0.0123210.011493-0.1493330.7706840.770646-0.452469-0.2130540.770767-0.1918030.7652550.1053260.772989-0.1911530.7660310.2150760.772896
price0.0008080.000561-0.0007220.0009131.0000000.3450610.2404580.002088-0.001417-0.000084-0.0001930.000166-0.000243-0.0012380.0009900.0012180.0008510.001497-0.0005160.0007590.0001820.000754-0.0002610.0007410.0008310.000761-0.0006150.000866-0.0013820.000883-0.0004240.0014970.0004600.0007600.000760-0.0016020.0011140.000763-0.0004280.000769-0.0005500.000766-0.0005800.000814-0.0002900.000746
distance0.0035530.002280-0.0006270.0022980.3450611.0000000.0247690.000819-0.000293-0.002884-0.003116-0.0002560.000371-0.0039010.0022770.0013640.0036180.004092-0.0041540.003604-0.0021460.003556-0.0040980.003585-0.0015010.003583-0.0040780.003490-0.002146-0.0009050.0023180.0040920.0014670.0035560.003556-0.0045940.0006480.003562-0.0033960.003428-0.0041910.003580-0.0039370.003526-0.0042380.003584
surge_multiplier0.000572-0.0000770.001389-0.0006280.2404580.0247691.0000000.001375-0.001829-0.001572-0.002532-0.001530-0.002769-0.0016760.0025110.0020930.0004390.001714-0.0018970.000520-0.0026010.000573-0.0020410.000520-0.0029940.000551-0.001849-0.0030030.002167-0.002103-0.0024220.0017140.0033240.0005440.000544-0.000150-0.0001010.000544-0.0007240.000554-0.0019510.000522-0.0013040.000464-0.0021540.000519
latitude0.1681300.0194930.0068380.0857910.0020880.0008190.0013751.000000-0.531259-0.098604-0.067098-0.074181-0.071275-0.117481-0.0971390.0243090.1726250.124166-0.1153070.173702-0.0321330.171193-0.0921670.173299-0.0023550.172571-0.1302340.149110-0.032318-0.0644640.0106980.1241660.0632310.1717170.171696-0.199310-0.0475350.171820-0.1141850.169592-0.1089530.175177-0.0960840.170921-0.0826000.173206
longitude-0.136802-0.0062950.078690-0.126912-0.001417-0.000293-0.001829-0.5312591.0000000.012183-0.0061030.1308400.1223550.0908470.092797-0.006170-0.137122-0.0970310.007527-0.1365720.078306-0.136020-0.001593-0.1362230.019534-0.1371850.056191-0.085665-0.0348770.0734580.010109-0.097031-0.022658-0.136500-0.1364820.1045840.030753-0.1367240.048723-0.140403-0.007274-0.1370410.010138-0.140518-0.022854-0.135621
temperature-0.2487480.218769-0.084116-0.081774-0.000084-0.002884-0.001572-0.0986040.0121831.0000000.9461660.1827240.2391650.3138530.058655-0.009082-0.247956-0.3240870.788706-0.2571690.502326-0.2542850.710552-0.2586080.382977-0.2534920.863992-0.339210-0.2443710.3551560.158526-0.324087-0.291277-0.254989-0.2549900.3453060.333081-0.2544670.789261-0.2426980.780546-0.2538810.817521-0.2315130.703208-0.255288
apparentTemperature-0.2262640.198190-0.2235450.024983-0.000193-0.003116-0.002532-0.067098-0.0061030.9461661.0000000.0983230.1587580.356516-0.244484-0.287506-0.223617-0.2992470.821653-0.2331170.574061-0.2336940.811792-0.2339530.495935-0.2313660.849737-0.134527-0.2937160.2948570.138395-0.299247-0.462467-0.231666-0.2316850.3243280.257072-0.2313090.697441-0.2247440.792625-0.2277120.786577-0.2124530.782396-0.228674
precipIntensity-0.179958-0.2333490.091664-0.1545310.000166-0.000256-0.001530-0.0741810.1308400.1827240.0983231.0000000.8384700.4175580.3073690.198077-0.175276-0.6006630.119129-0.1821790.231751-0.1816670.114575-0.1833100.183130-0.1805850.338755-0.143718-0.4333270.288960-0.099118-0.600663-0.222314-0.180938-0.1809170.0778610.498788-0.1802240.236578-0.1822330.114020-0.1820980.224691-0.1704300.108896-0.183115
precipProbability-0.147596-0.1297250.030903-0.098031-0.0002430.000371-0.002769-0.0712750.1223550.2391650.1587580.8384701.0000000.5487660.2521170.127639-0.143261-0.7612590.147960-0.1466400.246468-0.1457610.142714-0.1479530.186981-0.1442470.445270-0.179500-0.4701810.387114-0.071009-0.761259-0.235319-0.145520-0.1454980.0846240.583335-0.1446370.274337-0.1479880.142092-0.1465570.263250-0.1359670.136422-0.147471
humidity-0.168024-0.275762-0.093340-0.022256-0.001238-0.003901-0.001676-0.1174810.0908470.3138530.3565160.4175580.5487661.000000-0.207223-0.306439-0.159006-0.6978200.429947-0.1617070.471669-0.1647560.451301-0.1626220.357249-0.1616420.747336-0.133901-0.3568370.480199-0.208030-0.697820-0.426308-0.161199-0.1611880.1471120.520122-0.1603870.461731-0.1678380.394811-0.1582780.512270-0.1527850.410480-0.158422
windSpeed-0.0661710.0727780.440502-0.3348030.0009900.0022770.002511-0.0971390.0927970.058655-0.2444840.3073690.252117-0.2072231.0000000.937638-0.070913-0.035520-0.189063-0.071159-0.286503-0.060080-0.378994-0.072762-0.384416-0.064934-0.075004-0.5744580.1129380.1597750.067989-0.0355200.544955-0.068376-0.0683200.0409810.212282-0.0679810.173915-0.051350-0.120832-0.078361-0.020056-0.056391-0.304547-0.079521
windGust0.0057060.0773080.402637-0.2704940.0012180.0013640.0020930.024309-0.006170-0.009082-0.2875060.1980770.127639-0.3064390.9376381.0000000.0004970.038831-0.2311330.002263-0.3417250.012889-0.4167390.000975-0.4038400.007643-0.175517-0.5137800.1683330.1047960.0903390.0388310.5850720.0047030.0047520.0051080.1002430.0050060.0811420.023669-0.144780-0.005769-0.1167430.014164-0.316410-0.007036
windGustTime0.998842-0.028047-0.3364170.7672650.0008510.0036180.0004390.172625-0.137122-0.247956-0.223617-0.175276-0.143261-0.159006-0.0709130.0004971.0000000.152486-0.2212430.999252-0.3508510.998968-0.2089570.999163-0.3859550.999034-0.2599490.541717-0.150104-0.079717-0.0258800.1524860.1910380.9993570.999357-0.854560-0.2018320.999362-0.3009560.998324-0.1797220.999302-0.3715040.998958-0.1516960.999174
visibility0.1589930.1741290.1049920.0114930.0014970.0040920.0017140.124166-0.097031-0.324087-0.299247-0.600663-0.761259-0.697820-0.0355200.0388310.1524861.000000-0.2907260.154287-0.2638250.152967-0.2842800.155511-0.1967520.151688-0.5799150.2279120.401978-0.4776180.1198421.0000000.2631400.1528770.152864-0.173903-0.5450520.152129-0.3528690.152441-0.2971590.154674-0.3440160.142179-0.2977510.155300
temperatureHigh-0.2281250.005172-0.2835610.068313-0.000516-0.004154-0.001897-0.1153070.0075270.7887060.8216530.1191290.1479600.429947-0.189063-0.231133-0.221243-0.2907261.000000-0.2312430.611657-0.2314250.958746-0.2319910.534880-0.2297900.777892-0.144797-0.1920000.112706-0.012237-0.290726-0.489453-0.228636-0.2286610.3748490.192505-0.2282040.794548-0.2142140.986001-0.2259310.877466-0.2057080.951057-0.226911
temperatureHighTime0.999332-0.027838-0.3431180.7720530.0007590.0036040.0005200.173702-0.136572-0.257169-0.233117-0.182179-0.146640-0.161707-0.0711590.0022630.9992520.154287-0.2312431.000000-0.3659750.999695-0.2182180.999986-0.4003550.999716-0.2680950.535054-0.131782-0.079657-0.0231660.1542870.1953000.9999200.999920-0.843372-0.2144410.999909-0.3097500.998486-0.1887930.999885-0.3813870.998537-0.1597260.999885
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\n" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corr_num = num_data.corr()\n", "corr_num.style.background_gradient(cmap='RdBu')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "bisa dilihat kolom price memiliki korelasi dengan kolom distance dan surge_multiplier yaitu \n", "\n", "distance 0.345061, surge_multiplier 0.240458\n", "\n", "Maka kolom numerical yang akan digunakan sebagai feature adalah kolom distance dan surge_multiplier saja, karena dari hasil correlation matrix diatas nilai correlation kolom price dengan kolom lain selain distance dan surge_multiplier mendekati 0 ." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlation Between Categorical Columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "cek data categorical data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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iddatetimetimezonesourcedestinationcab_typeproduct_idnameshort_summarylong_summaryicon
0424553bb-7174-41ea-aeb4-fe06d4f4b9d72018-12-16 09:30:07America/New_YorkHaymarket SquareNorth StationLyftlyft_lineSharedMostly CloudyRain throughout the day.partly-cloudy-night
14bd23055-6827-41c6-b23b-3c491f24e74d2018-11-27 02:00:23America/New_YorkHaymarket SquareNorth StationLyftlyft_premierLuxRainRain until morning, starting again in the eve...rain
2981a3613-77af-4620-a42a-0c0866077d1e2018-11-28 01:00:22America/New_YorkHaymarket SquareNorth StationLyftlyftLyftClearLight rain in the morning.clear-night
3c2d88af2-d278-4bfd-a8d0-29ca77cc55122018-11-30 04:53:02America/New_YorkHaymarket SquareNorth StationLyftlyft_luxsuvLux Black XLClearPartly cloudy throughout the day.clear-night
4e0126e1f-8ca9-4f2e-82b3-50505a09db9a2018-11-29 03:49:20America/New_YorkHaymarket SquareNorth StationLyftlyft_plusLyft XLPartly CloudyMostly cloudy throughout the day.partly-cloudy-night
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 pricesource_Back Baysource_Beacon Hillsource_Boston Universitysource_Fenwaysource_Financial Districtsource_Haymarket Squaresource_North Endsource_North Stationsource_Northeastern Universitysource_South Stationsource_Theatre Districtsource_West Enddestination_Back Baydestination_Beacon Hilldestination_Boston Universitydestination_Fenwaydestination_Financial Districtdestination_Haymarket Squaredestination_North Enddestination_North Stationdestination_Northeastern Universitydestination_South Stationdestination_Theatre Districtdestination_West Endcab_type_Lyftcab_type_Ubername_Blackname_Black SUVname_Luxname_Lux Blackname_Lux Black XLname_Lyftname_Lyft XLname_Sharedname_Taxiname_UberPoolname_UberXname_UberXLname_WAVshort_summary_ Clear short_summary_ Drizzle short_summary_ Foggy short_summary_ Light Rain short_summary_ Mostly Cloudy short_summary_ Overcast short_summary_ Partly Cloudy short_summary_ Possible Drizzle short_summary_ Rain icon_ clear-day icon_ clear-night icon_ cloudy icon_ fog icon_ partly-cloudy-day icon_ partly-cloudy-night icon_ rain
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source_Back Bay-0.0161011.000000-0.090637-0.090947-0.090941-0.091882-0.090923-0.090946-0.090391-0.090940-0.090935-0.090989-0.090773-0.090961-0.0906370.0872020.087860-0.0918770.0904120.102137-0.0903920.0900360.087972-0.090976-0.0907850.000228-0.000228-0.000060-0.0000620.0000750.0000750.0000750.0000750.0000750.000058-0.000060-0.000054-0.000059-0.000062-0.000062-0.0008980.0011060.0009890.000447-0.0011100.002429-0.002098-0.0002570.000711-0.0027160.0008030.0024290.000989-0.000603-0.0024800.000898
source_Beacon Hill-0.028396-0.0906371.000000-0.090613-0.090607-0.091545-0.090589-0.090612-0.090059-0.090606-0.090601-0.090655-0.090440-0.090626-0.0903030.0840500.088119-0.0915400.0905650.099543-0.0900600.0928210.088516-0.090642-0.0904510.000034-0.000034-0.000004-0.0000050.0000100.0000100.0000100.0000100.0000100.000013-0.000023-0.000017-0.000002-0.000005-0.000005-0.000823-0.0012970.0047750.0014530.001184-0.0020920.000484-0.0016020.000175-0.0028860.001007-0.0020920.004775-0.0045420.0056310.000091
source_Boston University0.074634-0.090947-0.0906131.000000-0.090917-0.091858-0.090899-0.090922-0.090367-0.090916-0.090911-0.090965-0.0907490.0872290.083917-0.090923-0.0909170.090002-0.090923-0.0909160.089587-0.090915-0.0909100.0972330.097547-0.0000100.0000100.0000020.000000-0.000004-0.000004-0.000004-0.000004-0.000004-0.0000000.0000020.0000090.0000040.0000000.000000-0.0002130.000047-0.003590-0.0013270.002087-0.0015700.000749-0.0037830.0056780.001507-0.001282-0.001570-0.0035900.0006260.0021650.000186
source_Fenway0.059317-0.090941-0.090607-0.0909171.000000-0.091852-0.090893-0.090916-0.090361-0.090910-0.090905-0.090959-0.0907430.0880190.088119-0.090917-0.0909110.089456-0.090917-0.0909100.094747-0.090909-0.0909040.0836730.1015170.000023-0.000023-0.000006-0.0000080.0000070.0000070.0000070.0000070.0000070.000010-0.000006-0.000000-0.000005-0.000008-0.0000080.0005730.000716-0.000460-0.004389-0.0013230.0004870.003436-0.0010340.000688-0.0022200.0021930.000487-0.0004600.003375-0.001113-0.003226
source_Financial District0.053468-0.091882-0.091545-0.091858-0.0918521.000000-0.091834-0.091857-0.091297-0.091851-0.091846-0.091901-0.091683-0.091872-0.0915450.0899880.089704-0.0927980.0978900.083040-0.0912980.0884360.102027-0.091888-0.0916940.001368-0.001368-0.000359-0.0003610.0004360.0004360.0004360.0004360.0004360.000419-0.000359-0.000353-0.000358-0.000361-0.000361-0.000327-0.001805-0.0004740.0056680.001682-0.001314-0.002047-0.000371-0.0022400.001754-0.001585-0.001314-0.000474-0.000255-0.0000280.002457
source_Haymarket Square-0.095924-0.090923-0.090589-0.090899-0.090893-0.0918341.000000-0.090898-0.090343-0.090892-0.090887-0.090941-0.0907250.0905030.090629-0.090899-0.0908930.097989-0.090899-0.0908920.092574-0.090891-0.0908860.0911420.0824780.000058-0.000058-0.000013-0.0000140.0000180.0000180.0000180.0000180.0000180.000021-0.000013-0.000026-0.000011-0.000014-0.0000140.0002210.002671-0.001735-0.0016900.002125-0.0011340.0006830.000276-0.0018760.001482-0.000755-0.001134-0.0017350.0011540.001672-0.001342
source_North End-0.045006-0.090946-0.090612-0.090922-0.090916-0.091857-0.0908981.000000-0.090366-0.090915-0.090910-0.090964-0.0907490.1021490.099526-0.090922-0.0909160.083037-0.090922-0.0909150.088488-0.090914-0.0909090.0888900.083497-0.0000050.000005-0.0000160.000002-0.000002-0.000002-0.000002-0.000002-0.0000020.0000010.0000040.0000100.0000050.0000020.000002-0.0035020.001326-0.0017050.006812-0.0039490.001286-0.0011500.0014460.003324-0.001387-0.0031690.001286-0.001705-0.003744-0.0015770.007860
source_North Station-0.005821-0.090391-0.090059-0.090367-0.090361-0.091297-0.090343-0.0903661.000000-0.090360-0.090355-0.090409-0.090195-0.090381-0.0900590.0895890.094749-0.0912920.0926450.088487-0.0898160.0933870.083282-0.090396-0.090206-0.0000890.0000890.0000280.000027-0.000029-0.000029-0.000029-0.000029-0.000029-0.0000250.0000280.0000150.0000110.0000270.000027-0.0002900.0001400.0038040.0004770.000442-0.0008890.0000830.000946-0.0023720.000590-0.000745-0.0008890.0038040.003172-0.002310-0.000378
source_Northeastern University0.043847-0.090940-0.090606-0.090916-0.090910-0.091851-0.090892-0.090915-0.0903601.000000-0.090904-0.090958-0.0907430.0899660.092932-0.090916-0.0909100.088578-0.090916-0.0909090.093383-0.090908-0.0909030.0911530.089508-0.0000350.0000350.0000140.000013-0.000012-0.000012-0.000012-0.000012-0.000012-0.0000080.0000140.000001-0.0000030.0000130.0000130.002212-0.0020970.000919-0.0029570.002736-0.001328-0.0025350.004712-0.0005740.0021110.001158-0.0013280.0009190.003265-0.002578-0.000992
source_South Station-0.028216-0.090935-0.090601-0.090911-0.090905-0.091846-0.090887-0.090910-0.090355-0.0909041.000000-0.090953-0.0907370.0877330.088532-0.090911-0.0909050.102039-0.090911-0.0909040.083410-0.090903-0.0908980.0936020.0899950.000056-0.000056-0.000015-0.0000170.0000170.0000170.0000170.0000170.0000170.000020-0.000015-0.000009-0.000013-0.000017-0.0000170.0018940.000259-0.002341-0.000764-0.0019440.0027020.000595-0.000737-0.0041590.0031450.0000740.002702-0.002341-0.0017800.000239-0.002948
source_Theatre District0.001678-0.090989-0.090655-0.090965-0.090959-0.091901-0.090941-0.090964-0.090409-0.090958-0.0909531.000000-0.090791-0.090979-0.0906550.0971790.083508-0.0918960.0914590.088891-0.0904100.0911210.093570-0.090994-0.090803-0.0011830.0011830.0003130.000311-0.000375-0.000375-0.000375-0.000375-0.000375-0.0003720.0003130.0003000.0003140.0003110.0003110.0000210.000532-0.001458-0.0007060.000215-0.000082-0.0005730.0004990.0021260.000211-0.000120-0.000082-0.001458-0.0010580.0006200.000924
source_West End-0.014092-0.090773-0.090440-0.090749-0.090743-0.091683-0.090725-0.090749-0.090195-0.090743-0.090737-0.0907911.000000-0.090763-0.0904400.0974640.101548-0.0916780.0824450.083409-0.0901960.0896730.089894-0.090779-0.090587-0.0004560.0004560.0001190.000118-0.000145-0.000145-0.000145-0.000145-0.000145-0.0001420.0001190.0001260.0001210.0001180.0001180.001134-0.0015860.001313-0.003071-0.0021560.0015070.002399-0.000090-0.001477-0.0016120.0024370.0015070.0013130.000397-0.000236-0.003561
destination_Back Bay-0.010834-0.090961-0.0906260.0872290.088019-0.0918720.0905030.102149-0.0903810.0899660.087733-0.090979-0.0907631.000000-0.090626-0.090937-0.090931-0.091867-0.090937-0.090930-0.090382-0.090929-0.090924-0.090966-0.0907740.000231-0.000231-0.000061-0.0000630.0000730.0000730.0000730.0000730.0000730.000076-0.000061-0.000055-0.000059-0.000063-0.000063-0.004401-0.000064-0.0051610.001335-0.000067-0.0007510.0011000.0055940.004054-0.000025-0.005160-0.000751-0.0051610.001835-0.0006800.005576
destination_Beacon Hill-0.009565-0.090637-0.0903030.0839170.088119-0.0915450.0906290.099526-0.0900590.0929320.088532-0.090655-0.090440-0.0906261.000000-0.090613-0.090607-0.091540-0.090613-0.090606-0.090060-0.090605-0.090600-0.090642-0.0904510.000034-0.000034-0.000004-0.0000050.0000100.0000100.0000100.0000100.0000100.000013-0.000004-0.000017-0.000022-0.000005-0.0000050.003393-0.000015-0.002553-0.0003500.001851-0.002554-0.000476-0.000308-0.0004010.0029490.001973-0.002554-0.0025530.0002500.001139-0.000611
destination_Boston University0.0775150.0872020.084050-0.090923-0.0909170.089988-0.090899-0.0909220.089589-0.090916-0.0909110.0971790.097464-0.090937-0.0906131.000000-0.090917-0.091853-0.090923-0.090916-0.090368-0.090915-0.090910-0.090952-0.090761-0.0000730.0000730.0000210.000020-0.000024-0.000024-0.000024-0.000024-0.000024-0.0000200.0000210.0000090.0000230.0000200.0000200.000370-0.002050-0.0035900.004201-0.0020710.0010690.000897-0.001040-0.002592-0.0009010.0010520.001069-0.003590-0.000944-0.0003490.000799
destination_Fenway0.0517800.0878600.088119-0.090917-0.0909110.089704-0.090893-0.0909160.094749-0.090910-0.0909050.0835080.101548-0.090931-0.090607-0.0909171.000000-0.091847-0.090917-0.090910-0.090362-0.090909-0.090904-0.090946-0.0907550.000023-0.000023-0.000006-0.0000080.0000070.0000070.0000070.0000070.0000070.000010-0.000006-0.000000-0.000005-0.000008-0.000008-0.0020890.0017390.0062500.002550-0.001477-0.0020620.0019260.000677-0.000978-0.001111-0.001697-0.0020620.0062500.002043-0.0014730.002229
destination_Financial District0.049051-0.091877-0.0915400.0900020.089456-0.0927980.0979890.083037-0.0912920.0885780.102039-0.091896-0.091678-0.091867-0.091540-0.091853-0.0918471.000000-0.091853-0.091846-0.091292-0.091845-0.091840-0.091883-0.0916890.001406-0.001406-0.000369-0.0003710.0004440.0004440.0004440.0004440.0004440.000448-0.000369-0.000363-0.000368-0.000371-0.0003710.0001220.003777-0.001290-0.001509-0.000725-0.0011150.003249-0.000718-0.0010950.003290-0.002108-0.001115-0.0012900.0011760.001248-0.000943
destination_Haymarket Square-0.0740400.0904120.090565-0.090923-0.0909170.097890-0.090899-0.0909220.092645-0.090916-0.0909110.0914590.082445-0.090937-0.090613-0.090923-0.090917-0.0918531.000000-0.090916-0.090368-0.090915-0.090910-0.090952-0.090761-0.0000830.0000830.0000210.000020-0.000024-0.000024-0.000024-0.000024-0.000024-0.0000400.0000210.0000280.0000230.0000200.000020-0.002055-0.0006690.0024770.0001620.0024330.000159-0.000182-0.002718-0.000726-0.001578-0.0013370.0001590.0024770.0011720.001157-0.001666
destination_North End-0.0498910.1021370.099543-0.090916-0.0909100.083040-0.090892-0.0909150.088487-0.090909-0.0909040.0888910.083409-0.090930-0.090606-0.090916-0.090910-0.091846-0.0909161.000000-0.090361-0.090908-0.090903-0.090945-0.090754-0.0000350.0000350.0000140.000013-0.000012-0.000012-0.000012-0.000012-0.000012-0.0000080.0000140.000001-0.0000030.0000130.000013-0.0039610.0002560.004688-0.0021460.0005860.001681-0.0011600.0034530.001120-0.003220-0.0024540.0016810.0046880.000056-0.0005500.000583
destination_North Station0.008360-0.090392-0.0900600.0895870.094747-0.0912980.0925740.088488-0.0898160.0933830.083410-0.090410-0.090196-0.090382-0.090060-0.090368-0.090362-0.091292-0.090368-0.0903611.000000-0.090360-0.090355-0.090397-0.090207-0.0000930.0000930.0000070.000025-0.000030-0.000030-0.000030-0.000030-0.000030-0.0000270.0000270.0000330.0000280.0000250.0000250.000705-0.0022260.000662-0.0022640.0000800.003058-0.003254-0.0003850.002188-0.0010970.0015810.0030580.000662-0.001732-0.001387-0.001406
destination_Northeastern University0.0414670.0900360.092821-0.090915-0.0909090.088436-0.090891-0.0909140.093387-0.090908-0.0909030.0911210.089673-0.090929-0.090605-0.090915-0.090909-0.091845-0.090915-0.090908-0.0903601.000000-0.090902-0.090944-0.0907530.000022-0.000022-0.000003-0.0000050.0000100.0000100.0000100.0000100.000010-0.000007-0.0000230.000003-0.000002-0.000005-0.000005-0.000904-0.0002550.0007820.0002910.0012540.002123-0.002641-0.000128-0.0016360.003194-0.0032490.0021230.000782-0.0022820.000809-0.000742
destination_South Station-0.0555040.0879720.088516-0.090910-0.0909040.102027-0.090886-0.0909090.083282-0.090903-0.0908980.0935700.089894-0.090924-0.090600-0.090910-0.090904-0.091840-0.090910-0.090903-0.090355-0.0909021.000000-0.090939-0.0907480.000060-0.000060-0.000014-0.0000150.0000190.0000190.0000190.0000190.0000190.000022-0.000014-0.000026-0.000012-0.000015-0.0000150.007454-0.001940-0.001696-0.000743-0.000457-0.003415-0.000617-0.0011240.001730-0.0010210.009468-0.003415-0.001696-0.0029410.001598-0.000743
destination_Theatre District-0.018458-0.090976-0.0906420.0972330.083673-0.0918880.0911420.088890-0.0903960.0911530.093602-0.090994-0.090779-0.090966-0.090642-0.090952-0.090946-0.091883-0.090952-0.090945-0.090397-0.090944-0.0909391.000000-0.090790-0.0010500.0010500.0002780.000276-0.000333-0.000333-0.000333-0.000333-0.000333-0.0003300.0002780.0002650.0002800.0002760.0002760.0020500.0009490.000204-0.0016680.000972-0.0014540.000516-0.0033270.0014800.004663-0.000781-0.0014540.0002040.002549-0.000843-0.001740
destination_West End-0.010306-0.090785-0.0904510.0975470.101517-0.0916940.0824780.083497-0.0902060.0895080.089995-0.090803-0.090587-0.090774-0.090451-0.090761-0.090755-0.091689-0.090761-0.090754-0.090207-0.090753-0.090748-0.0907901.000000-0.0004530.0004530.0001190.000117-0.000144-0.000144-0.000144-0.000144-0.000144-0.0001410.0001190.0001250.0001200.0001170.000117-0.0006740.000456-0.0007660.000142-0.0023710.0032830.0005970.000028-0.003129-0.0051760.0027500.003283-0.000766-0.001205-0.000684-0.001340
cab_type_Lyft0.0833850.0002280.000034-0.0000100.0000230.0013680.000058-0.000005-0.000089-0.0000350.000056-0.001183-0.0004560.0002310.000034-0.0000730.0000230.001406-0.000083-0.000035-0.0000930.0000220.000060-0.001050-0.0004531.000000-1.000000-0.262366-0.2623690.3164590.3164590.3164590.3164590.3164590.316453-0.262366-0.262356-0.262363-0.262369-0.2623690.000076-0.003560-0.000422-0.000300-0.0009300.0020380.001751-0.003481-0.0011880.000795-0.0004540.002038-0.000422-0.0004040.001070-0.003418
cab_type_Uber-0.083385-0.000228-0.0000340.000010-0.000023-0.001368-0.0000580.0000050.0000890.000035-0.0000560.0011830.000456-0.000231-0.0000340.000073-0.000023-0.0014060.0000830.0000350.000093-0.000022-0.0000600.0010500.000453-1.0000001.0000000.2623660.262369-0.316459-0.316459-0.316459-0.316459-0.316459-0.3164530.2623660.2623560.2623630.2623690.262369-0.0000760.0035600.0004220.0003000.000930-0.002038-0.0017510.0034810.001188-0.0007950.000454-0.0020380.0004220.000404-0.0010700.003418
name_Black0.131185-0.000060-0.0000040.000002-0.000006-0.000359-0.000013-0.0000160.0000280.000014-0.0000150.0003130.000119-0.000061-0.0000040.000021-0.000006-0.0003690.0000210.0000140.000007-0.000003-0.0000140.0002780.000119-0.2623660.2623661.000000-0.086360-0.083028-0.083028-0.083028-0.083028-0.083028-0.083026-0.086359-0.086356-0.086358-0.086360-0.086360-0.0008370.0004710.0002250.0011020.001597-0.002121-0.0006000.0007110.001967-0.000110-0.000909-0.0021210.0002250.0000440.0009610.002286
name_Black SUV0.453096-0.000062-0.0000050.000000-0.000008-0.000361-0.0000140.0000020.0000270.000013-0.0000170.0003110.000118-0.000063-0.0000050.000020-0.000008-0.0003710.0000200.0000130.000025-0.000005-0.0000150.0002760.000117-0.2623690.262369-0.0863601.000000-0.083029-0.083029-0.083029-0.083029-0.083029-0.083027-0.086360-0.086356-0.086359-0.086361-0.086361-0.0013530.001254-0.000058-0.0000840.0007580.000457-0.0013050.0014350.000558-0.001245-0.0007400.000457-0.000058-0.000044-0.0004280.001226
name_Lux0.0388570.0000750.000010-0.0000040.0000070.0004360.000018-0.000002-0.000029-0.0000120.000017-0.000375-0.0001450.0000730.000010-0.0000240.0000070.000444-0.000024-0.000012-0.0000300.0000100.000019-0.000333-0.0001440.316459-0.316459-0.083028-0.0830291.000000-0.079826-0.079826-0.079826-0.079826-0.079824-0.083028-0.083025-0.083027-0.083029-0.0830290.000902-0.0011540.000643-0.000170-0.000183-0.0006020.000584-0.0008070.0002760.0006130.000642-0.0006020.000643-0.0005060.000811-0.000682
name_Lux Black0.2065440.0000750.000010-0.0000040.0000070.0004360.000018-0.000002-0.000029-0.0000120.000017-0.000375-0.0001450.0000730.000010-0.0000240.0000070.000444-0.000024-0.000012-0.0000300.0000100.000019-0.000333-0.0001440.316459-0.316459-0.083028-0.083029-0.0798261.000000-0.079826-0.079826-0.079826-0.079824-0.083028-0.083025-0.083027-0.083029-0.083029-0.0000790.000197-0.0003770.000034-0.0009260.0010230.001809-0.003363-0.0011800.000213-0.0002390.001023-0.0003770.0001750.000614-0.002038
name_Lux Black XL0.5000570.0000750.000010-0.0000040.0000070.0004360.000018-0.000002-0.000029-0.0000120.000017-0.000375-0.0001450.0000730.000010-0.0000240.0000070.000444-0.000024-0.000012-0.0000300.0000100.000019-0.000333-0.0001440.316459-0.316459-0.083028-0.083029-0.079826-0.0798261.000000-0.079826-0.079826-0.079824-0.083028-0.083025-0.083027-0.083029-0.083029-0.000961-0.0023420.001274-0.000273-0.0004530.0017230.000456-0.001284-0.0005430.000042-0.0011590.0017230.001274-0.0002690.000219-0.001730
name_Lyft-0.2197560.0000750.000010-0.0000040.0000070.0004360.000018-0.000002-0.000029-0.0000120.000017-0.000375-0.0001450.0000730.000010-0.0000240.0000070.000444-0.000024-0.000012-0.0000300.0000100.000019-0.000333-0.0001440.316459-0.316459-0.083028-0.083029-0.079826-0.079826-0.0798261.000000-0.079826-0.079824-0.083028-0.083025-0.083027-0.083029-0.083029-0.000062-0.000722-0.000862-0.0013750.0001150.0010230.001083-0.000636-0.0015140.000413-0.0003560.001023-0.0008620.0005150.000653-0.002300
name_Lyft XL-0.0391630.0000750.000010-0.0000040.0000070.0004360.000018-0.000002-0.000029-0.0000120.000017-0.000375-0.0001450.0000730.000010-0.0000240.0000070.000444-0.000024-0.000012-0.0000300.0000100.000019-0.000333-0.0001440.316459-0.316459-0.083028-0.083029-0.079826-0.079826-0.079826-0.0798261.000000-0.079824-0.083028-0.083025-0.083027-0.083029-0.083029-0.000312-0.000181-0.0011050.000871-0.0008860.001095-0.0006840.0001140.000610-0.000016-0.0003560.001095-0.001105-0.000417-0.0011210.000967
name_Shared-0.3332350.0000580.000013-0.0000000.0000100.0004190.0000210.000001-0.000025-0.0000080.000020-0.000372-0.0001420.0000760.000013-0.0000200.0000100.000448-0.000040-0.000008-0.000027-0.0000070.000022-0.000330-0.0001410.316453-0.316453-0.083026-0.083027-0.079824-0.079824-0.079824-0.079824-0.0798241.000000-0.083026-0.083023-0.083026-0.083027-0.0830270.000657-0.002558-0.0003750.0003430.000566-0.0003930.000077-0.0006340.0000960.0002440.000606-0.000393-0.000375-0.0002640.000856-0.000708
name_Taxinan-0.000060-0.0000230.000002-0.000006-0.000359-0.0000130.0000040.0000280.000014-0.0000150.0003130.000119-0.000061-0.0000040.000021-0.000006-0.0003690.0000210.0000140.000027-0.000023-0.0000140.0002780.000119-0.2623660.262366-0.086359-0.086360-0.083028-0.083028-0.083028-0.083028-0.083028-0.0830261.000000-0.086356-0.086358-0.086360-0.086360-0.000901-0.0004700.0022440.001162-0.0002070.000748-0.001592-0.0007070.0013510.000553-0.0014390.0007480.002244-0.001531-0.0003110.001109
name_UberPool-0.256930-0.000054-0.0000170.000009-0.000000-0.000353-0.0000260.0000100.0000150.000001-0.0000090.0003000.000126-0.000055-0.0000170.000009-0.000000-0.0003630.0000280.0000010.0000330.000003-0.0000260.0002650.000125-0.2623560.262356-0.086356-0.086356-0.083025-0.083025-0.083025-0.083025-0.083025-0.083023-0.0863561.000000-0.086355-0.086356-0.0863560.0015210.002616-0.000618-0.000925-0.000732-0.0009710.0002920.001308-0.0001420.0008620.001199-0.000971-0.0006180.000182-0.0006040.000567
name_UberX-0.223551-0.000059-0.0000020.000004-0.000005-0.000358-0.0000110.0000050.000011-0.000003-0.0000130.0003140.000121-0.000059-0.0000220.000023-0.000005-0.0003680.000023-0.0000030.000028-0.000002-0.0000120.0002800.000120-0.2623630.262363-0.086358-0.086359-0.083027-0.083027-0.083027-0.083027-0.083027-0.083026-0.086358-0.0863551.000000-0.086359-0.0863590.0001470.0006810.0002250.000433-0.000858-0.0002700.0000500.002459-0.0011130.000748-0.000339-0.0002700.000225-0.000383-0.0004480.001067
name_UberXL-0.028587-0.000062-0.0000050.000000-0.000008-0.000361-0.0000140.0000020.0000270.000013-0.0000170.0003110.000118-0.000063-0.0000050.000020-0.000008-0.0003710.0000200.0000130.000025-0.000005-0.0000150.0002760.000117-0.2623690.262369-0.086360-0.086361-0.083029-0.083029-0.083029-0.083029-0.083029-0.083027-0.086360-0.086356-0.0863591.000000-0.0863610.0007540.000523-0.000762-0.0009530.001686-0.0018840.0003480.000644-0.000059-0.0009410.001531-0.001884-0.0007620.0010150.001059-0.000309
name_WAV-0.223557-0.000062-0.0000050.000000-0.000008-0.000361-0.0000140.0000020.0000270.000013-0.0000170.0003110.000118-0.000063-0.0000050.000020-0.000008-0.0003710.0000200.0000130.000025-0.000005-0.0000150.0002760.000117-0.2623690.262369-0.086360-0.086361-0.083029-0.083029-0.083029-0.083029-0.083029-0.083027-0.086360-0.086356-0.086359-0.0863611.0000000.0005290.001463-0.000480-0.000183-0.0005360.000297-0.0004100.000545-0.000381-0.0013280.0015310.000297-0.0004800.001459-0.0021950.000332
short_summary_ Clear -0.002387-0.000898-0.000823-0.0002130.000573-0.0003270.000221-0.003502-0.0002900.0022120.0018940.0000210.001134-0.0044010.0033930.000370-0.0020890.000122-0.002055-0.0039610.000705-0.0009040.0074540.002050-0.0006740.000076-0.000076-0.000837-0.0013530.000902-0.000079-0.000961-0.000062-0.0003120.000657-0.0009010.0015210.0001470.0007540.0005291.000000-0.039112-0.043641-0.111231-0.196068-0.257635-0.179801-0.063032-0.0713690.5292420.814056-0.257635-0.043641-0.169484-0.206079-0.159828
short_summary_ Drizzle -0.0004800.001106-0.0012970.0000470.000716-0.0018050.0026710.0013260.000140-0.0020970.0002590.000532-0.001586-0.000064-0.000015-0.0020500.0017390.003777-0.0006690.000256-0.002226-0.000255-0.0019400.0009490.000456-0.0035600.0035600.0004710.001254-0.0011540.000197-0.002342-0.000722-0.000181-0.002558-0.0004700.0026160.0006810.0005230.001463-0.0391121.000000-0.011871-0.030257-0.053334-0.070081-0.048909-0.017146-0.019414-0.020700-0.031839-0.070081-0.011871-0.046102-0.0560570.244712
short_summary_ Foggy -0.0000520.0009890.004775-0.003590-0.000460-0.000474-0.001735-0.0017050.0038040.000919-0.002341-0.0014580.001313-0.005161-0.002553-0.0035900.006250-0.0012900.0024770.0046880.0006620.000782-0.0016960.000204-0.000766-0.0004220.0004220.000225-0.0000580.000643-0.0003770.001274-0.000862-0.001105-0.0003750.002244-0.0006180.000225-0.000762-0.000480-0.043641-0.0118711.000000-0.033760-0.059509-0.078195-0.054572-0.019131-0.021661-0.023096-0.035526-0.0781951.000000-0.051440-0.062547-0.048510
short_summary_ Light Rain 0.0005080.0004470.001453-0.001327-0.0043890.005668-0.0016900.0068120.000477-0.002957-0.000764-0.000706-0.0030710.001335-0.0003500.0042010.002550-0.0015090.000162-0.002146-0.0022640.000291-0.000743-0.0016680.000142-0.0003000.0003000.001102-0.000084-0.0001700.000034-0.000273-0.0013750.0008710.0003430.001162-0.0009250.000433-0.000953-0.000183-0.111231-0.030257-0.0337601.000000-0.151677-0.199305-0.139093-0.048761-0.055211-0.058868-0.090548-0.199305-0.033760-0.131112-0.1594210.695943
short_summary_ Mostly Cloudy 0.002734-0.0011100.0011840.002087-0.0013230.0016820.002125-0.0039490.0004420.002736-0.0019440.000215-0.002156-0.0000670.001851-0.002071-0.001477-0.0007250.0024330.0005860.0000800.001254-0.0004570.000972-0.002371-0.0009300.0009300.0015970.000758-0.000183-0.000926-0.0004530.000115-0.0008860.000566-0.000207-0.000732-0.0008580.001686-0.000536-0.196068-0.053334-0.059509-0.1516771.000000-0.351316-0.245180-0.085952-0.097321-0.103768-0.159611-0.351316-0.0595090.3432690.441405-0.217944
short_summary_ Overcast -0.0007290.002429-0.002092-0.0015700.000487-0.001314-0.0011340.001286-0.000889-0.0013280.002702-0.0000820.001507-0.000751-0.0025540.001069-0.002062-0.0011150.0001590.0016810.0030580.002123-0.003415-0.0014540.0032830.002038-0.002038-0.0021210.000457-0.0006020.0010230.0017230.0010230.001095-0.0003930.000748-0.000971-0.000270-0.0018840.000297-0.257635-0.070081-0.078195-0.199305-0.3513161.000000-0.322169-0.112942-0.127880-0.136351-0.2097301.000000-0.078195-0.303683-0.369253-0.286381
short_summary_ Partly Cloudy 0.000350-0.0020980.0004840.0007490.003436-0.0020470.000683-0.0011500.000083-0.0025350.000595-0.0005730.0023990.001100-0.0004760.0008970.0019260.003249-0.000182-0.001160-0.003254-0.002641-0.0006170.0005160.0005970.001751-0.001751-0.000600-0.0013050.0005840.0018090.0004560.001083-0.0006840.000077-0.0015920.0002920.0000500.000348-0.000410-0.179801-0.048909-0.054572-0.139093-0.245180-0.3221691.000000-0.078821-0.089246-0.095158-0.146368-0.322169-0.0545720.3372890.384804-0.199862
short_summary_ Possible Drizzle -0.001137-0.000257-0.001602-0.003783-0.001034-0.0003710.0002760.0014460.0009460.004712-0.0007370.000499-0.0000900.005594-0.000308-0.0010400.000677-0.000718-0.0027180.003453-0.000385-0.000128-0.001124-0.0033270.000028-0.0034810.0034810.0007110.001435-0.000807-0.003363-0.001284-0.0006360.000114-0.000634-0.0007070.0013080.0024590.0006440.000545-0.063032-0.017146-0.019131-0.048761-0.085952-0.112942-0.0788211.000000-0.031287-0.033359-0.051312-0.112942-0.019131-0.074298-0.0903410.394376
short_summary_ Rain -0.0001040.0007110.0001750.0056780.000688-0.002240-0.0018760.003324-0.002372-0.000574-0.0041590.002126-0.0014770.004054-0.000401-0.002592-0.000978-0.001095-0.0007260.0011200.002188-0.0016360.0017300.001480-0.003129-0.0011880.0011880.0019670.0005580.000276-0.001180-0.000543-0.0015140.0006100.0000960.001351-0.000142-0.001113-0.000059-0.000381-0.071369-0.019414-0.021661-0.055211-0.097321-0.127880-0.089246-0.0312871.000000-0.037772-0.058099-0.127880-0.021661-0.084125-0.1022890.446538
icon_ clear-day -0.000668-0.002716-0.0028860.001507-0.0022200.0017540.001482-0.0013870.0005900.0021110.0031450.000211-0.001612-0.0000250.002949-0.000901-0.0011110.003290-0.001578-0.003220-0.0010970.003194-0.0010210.004663-0.0051760.000795-0.000795-0.000110-0.0012450.0006130.0002130.0000420.000413-0.0000160.0002440.0005530.0008620.000748-0.000941-0.0013280.529242-0.020700-0.023096-0.058868-0.103768-0.136351-0.095158-0.033359-0.0377721.000000-0.061947-0.136351-0.023096-0.089698-0.109066-0.084588
icon_ clear-night -0.0023500.0008030.001007-0.0012820.002193-0.001585-0.000755-0.003169-0.0007450.0011580.000074-0.0001200.002437-0.0051600.0019730.001052-0.001697-0.002108-0.001337-0.0024540.001581-0.0032490.009468-0.0007810.002750-0.0004540.000454-0.000909-0.0007400.000642-0.000239-0.001159-0.000356-0.0003560.000606-0.0014390.001199-0.0003390.0015310.0015310.814056-0.031839-0.035526-0.090548-0.159611-0.209730-0.146368-0.051312-0.058099-0.0619471.000000-0.209730-0.035526-0.137970-0.167760-0.130109
icon_ cloudy -0.0007290.002429-0.002092-0.0015700.000487-0.001314-0.0011340.001286-0.000889-0.0013280.002702-0.0000820.001507-0.000751-0.0025540.001069-0.002062-0.0011150.0001590.0016810.0030580.002123-0.003415-0.0014540.0032830.002038-0.002038-0.0021210.000457-0.0006020.0010230.0017230.0010230.001095-0.0003930.000748-0.000971-0.000270-0.0018840.000297-0.257635-0.070081-0.078195-0.199305-0.3513161.000000-0.322169-0.112942-0.127880-0.136351-0.2097301.000000-0.078195-0.303683-0.369253-0.286381
icon_ fog -0.0000520.0009890.004775-0.003590-0.000460-0.000474-0.001735-0.0017050.0038040.000919-0.002341-0.0014580.001313-0.005161-0.002553-0.0035900.006250-0.0012900.0024770.0046880.0006620.000782-0.0016960.000204-0.000766-0.0004220.0004220.000225-0.0000580.000643-0.0003770.001274-0.000862-0.001105-0.0003750.002244-0.0006180.000225-0.000762-0.000480-0.043641-0.0118711.000000-0.033760-0.059509-0.078195-0.054572-0.019131-0.021661-0.023096-0.035526-0.0781951.000000-0.051440-0.062547-0.048510
icon_ partly-cloudy-day 0.001169-0.000603-0.0045420.0006260.003375-0.0002550.001154-0.0037440.0031720.003265-0.001780-0.0010580.0003970.0018350.000250-0.0009440.0020430.0011760.0011720.000056-0.001732-0.002282-0.0029410.002549-0.001205-0.0004040.0004040.000044-0.000044-0.0005060.000175-0.0002690.000515-0.000417-0.000264-0.0015310.000182-0.0003830.0010150.001459-0.169484-0.046102-0.051440-0.1311120.343269-0.3036830.337289-0.074298-0.084125-0.089698-0.137970-0.303683-0.0514401.000000-0.242911-0.188394
icon_ partly-cloudy-night 0.001944-0.0024800.0056310.002165-0.001113-0.0000280.001672-0.001577-0.002310-0.0025780.0002390.000620-0.000236-0.0006800.001139-0.000349-0.0014730.0012480.001157-0.000550-0.0013870.0008090.001598-0.000843-0.0006840.001070-0.0010700.000961-0.0004280.0008110.0006140.0002190.000653-0.0011210.000856-0.000311-0.000604-0.0004480.001059-0.002195-0.206079-0.056057-0.062547-0.1594210.441405-0.3692530.384804-0.090341-0.102289-0.109066-0.167760-0.369253-0.062547-0.2429111.000000-0.229072
icon_ rain -0.0003210.0008980.0000910.000186-0.0032260.002457-0.0013420.007860-0.000378-0.000992-0.0029480.000924-0.0035610.005576-0.0006110.0007990.002229-0.000943-0.0016660.000583-0.001406-0.000742-0.000743-0.001740-0.001340-0.0034180.0034180.0022860.001226-0.000682-0.002038-0.001730-0.0023000.000967-0.0007080.0011090.0005670.001067-0.0003090.000332-0.1598280.244712-0.0485100.695943-0.217944-0.286381-0.1998620.3943760.446538-0.084588-0.130109-0.286381-0.048510-0.188394-0.2290721.000000
\n" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# korelasikan price dengan categorical column\n", "corr_cat = pd.get_dummies(data[['price','source','destination','cab_type','name','short_summary','icon']]).corr()\n", "corr_cat.style.background_gradient(cmap='RdBu')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "bisa dilihat kolom price memiliki korelasi dengan kolom name yaitu \n", "\n", "name_Black SUV 0.453096, name_Lux Black 0.206544, name_Lux Black XL\t0.500057\t\n", "\n", "Maka kolom categorical yang akan digunakan sebagai feature hanya kolom name saja, karena dari hasil correlation matrix diatas nilai correlation kolom price dengan kolom lain selain name mendekati 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Price Uber Vs Lyft" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pisahkan data uber dengan lyft" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "lyft_data = data[data[\"cab_type\"] == \"Lyft\"]\n", "uber_data = data[data[\"cab_type\"] == \"Uber\"]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Skewness: 1.0457470560899256\n", "Mean: 16.545125490614065\n", "Median: 13.5\n", "Mean Uber: 17.351396125019512\n", "Mean Lyft: 15.795343166912708\n" ] } ], "source": [ "plt.figure(figsize=(25,6))\n", "plt.subplot(1, 3, 1)\n", "sns.histplot(lyft_data, x='price',label='lyft',color='navy')\n", "sns.histplot(uber_data, x='price',label='uber', color='lightcoral')\n", "plt.title('Uber Vs Lyft')\n", "plt.xlabel('Price')\n", "plt.ylabel('Frequency')\n", "plt.legend()\n", "plt.subplot(1, 3, 2)\n", "sns.histplot(data, x='price',label='all data', color='red')\n", "plt.axvline(data.price.mean(), color='red', linestyle='dashed', linewidth=2, label='Mean')\n", "plt.axvline(data.price.median(), color='green', linestyle='dashed', linewidth=2, label='Median')\n", "plt.title('Price Distribution')\n", "plt.xlabel('Price')\n", "plt.ylabel('Frequency')\n", "plt.subplot(1, 3, 3)\n", "sns.boxplot(x='price', data=data, )\n", "plt.title('Price Distribution')\n", "plt.xlabel('Price')\n", "plt.ylabel('Frequency')\n", "plt.show()\n", "print(f'Skewness: {data.price.skew()}')\n", "print(f'Mean: {data.price.mean()}')\n", "print(f'Median: {data.price.median()}')\n", "\n", "print(f'Mean Uber: {lyft_data.price.mean()}')\n", "print(f'Mean Lyft: {uber_data.price.mean()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bisa diliat dari visualiasi diatas bahwa kolom price memiliki skewness sebesar 1.04 yang berarti positively skewed. dan dari boxplot bisa diliat bahwa kolom price memiliki outlier\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Korelasi Price dengan Surge_Multiplier Uber Vs Lyft" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.scatterplot(data=data, x=\"price\", y=\"surge_multiplier\", hue=\"cab_type\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Surge multiplier terjadi ketika sebuah perusahaan menaikkan harga penawarannya jika ada peningkatan permintaan. \n", "\n", "Misalnya, Lyft mengumumkan bahwa mereka akan menerapkan \"Surge multiplier\" pada Malam Natal dan Tahun Baru." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dari visualisasi tabel diatas bisa diliat bahwa yang memiliki surge multiplier hanyalah Lyft dari 1.0 sampai 3.0 sedangkan uber tidak memiliki surge multiplier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Korelasi Price dengan Distance Uber Vs Lyft" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.scatterplot(data=data, x=\"distance\", y=\"price\", hue=\"cab_type\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dari visualisasi tabel diatas bisa diliat bahwa\n", "- Lyft lebih mahal dari uber dikarenakan Surge multiplier\n", "- Distance paling jauh Lyft adalah 6.33 sedangkan distance paling jauh Uber adalah 7.86" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Product ID dan Name" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "product_id name \n", "55c66225-fbe7-4fd5-9072-eab1ece5e23e UberX 55094\n", "6c84fd89-3f11-4782-9b50-97c468b19529 Black 55095\n", "6d318bcc-22a3-4af6-bddd-b409bfce1546 Black SUV 55096\n", "6f72dfc5-27f1-42e8-84db-ccc7a75f6969 UberXL 55096\n", "8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a Taxi 55095\n", "997acbb5-e102-41e1-b155-9df7de0a73f2 UberPool 55091\n", "9a0e7b09-b92b-4c41-9779-2ad22b4d779d WAV 55096\n", "lyft Lyft 51235\n", "lyft_line Shared 51233\n", "lyft_lux Lux Black 51235\n", "lyft_luxsuv Lux Black XL 51235\n", "lyft_plus Lyft XL 51235\n", "lyft_premier Lux 51235\n", "dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.groupby(['product_id', 'name']).size()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tabel diatas menunjukkan bahwa name telah merepresentasikan product_id, maka product id tidak perlu dimasukkan kedalam feature" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hour, Day, Month Uber vs Lyft" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(25,10))\n", "plt.subplot(1, 3, 1)\n", "sns.countplot(x='hour', data=data, hue='cab_type')\n", "plt.title('Number of Rides per Hour')\n", "plt.xlabel('Hour')\n", "plt.ylabel('Frequency')\n", "plt.subplot(1, 3, 2)\n", "sns.countplot(x='day', data=data, hue='cab_type')\n", "plt.title('Number of Rides per Day')\n", "plt.xlabel('Day')\n", "plt.ylabel('Frequency')\n", "plt.subplot(1, 3, 3)\n", "sns.countplot(x='month', data=data, hue='cab_type' )\n", "plt.title('Number of Rides per Month')\n", "plt.xlabel('Month')\n", "plt.ylabel('Frequency')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Berdasarkan visualisasi data diatas, terlihat bahwa pemesanan didominasi oleh tipe Uber dibandingkan dengan Lyft untuk melihat kenapa uber mendominasi kita lihat perbandingan data uber dan lyft di visualisasi dibawah" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(25,6))\n", "plt.subplot(1, 3, 1)\n", "cab = sns.countplot(x='cab_type', data=data, order=data.cab_type.value_counts().index)\n", "for p in cab.patches:\n", " cab.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n", "plt.title('Number of Rides per Cab Type')\n", "plt.xlabel('Cab Type')\n", "plt.ylabel('Frequency')\n", "plt.subplot(1, 3, 2)\n", "sns.countplot(x='name', data=data, hue='cab_type')\n", "plt.title('Number of Rides per Product Name')\n", "plt.xlabel('Name')\n", "plt.ylabel('Frequency')\n", "plt.xticks(rotation=90)\n", "plt.legend(loc='upper left')\n", "plt.subplot(1, 3, 3)\n", "sns.boxplot(x='name', y='price', data=data, hue='cab_type')\n", "plt.title('Price Distribution per Product Name')\n", "plt.xlabel('Name')\n", "plt.ylabel('Price')\n", "plt.xticks(rotation=90)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "55.645525494502" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#presentase Uber\n", "385663/693071*100" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "44.35447450549799" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#presentase Lyft\n", "307408/693071*100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### insight yang didapat dan hal yang menarik" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Terlihat bahwa uber mendapatkan pemesanan sebanyak 385663 yaitu 55.64% dari total dataset sedangkan Lyft memiliki pemesanan sebanyak 307408 yaitu 44.35% dari total dataset ini yang membuat uber mendominasi pemesanan dari mulai jam,hari dan bulan.\n", "\n", "yang menariknya adalah tidak ada satu hari pun lyft mengalahkan pemesanan uber" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "dan dari box plot menunjukan bahwa produk yang dipilih oleh penumpang akan mempengaruhi price" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Desember mempunyai lebih banyak data dari November karena range data dari tanggal 11-26-2018 to 12-18-2018\n", "\n", "5 hari di bulan November dan 18 hari di bulan Desember" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dari Visualisasi tersebut yang menarik perhatian saya adalah ada apa dengan tanggal 9 dan 10, yang menyebabkan pemesanan uber dan lyft menurun drastis\n", "\n", "ternyata adanya missing data dari tanggal 5 sampai tanggal 8 dan berpengaruh juga di tanggal 4, 9 dan 10. sehingga terkesan bahwa tanggal 9 dan 10 mengalami penurunan drastis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## v. Data Preprocessing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get Data for Model Inference" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idtimestamphourdaymonthdatetimetimezonesourcedestinationcab_type...precipIntensityMaxuvIndexTimetemperatureMintemperatureMinTimetemperatureMaxtemperatureMaxTimeapparentTemperatureMinapparentTemperatureMinTimeapparentTemperatureMaxapparentTemperatureMaxTime
41849866a79fdc-1595-42b3-b28f-285aaebfcaec1.544883e+091415122018-12-15 14:10:03America/New_YorkTheatre DistrictBoston UniversityLyft...0.0074154489320039.54154492920054.47154489680036.46154492920053.801544896800
599390cf48b3da-90d2-4bdd-a3f9-a0aa132ec1651.544912e+092215122018-12-15 22:15:10America/New_YorkFinancial DistrictNorth EndUber...0.0074154489320039.48154492920054.47154489680036.40154492920053.801544896800
209422ea45cca5-81f9-47c5-93ec-e7d82e68a9f61.543492e+091129112018-11-29 11:42:56America/New_YorkNorth EndNorth StationLyft...0.0000154350720034.67154355040045.03154351080030.30154355040038.531543510800
262561352a8c51-907c-46bc-b622-85357609c9881.543778e+09192122018-12-02 19:17:57America/New_YorkSouth StationNorth StationUber...0.0916154377000036.32154372680050.80154378800035.84154374840050.131543788000
10863668581606-3daf-46ac-a09f-cac09a9f76dc1.544705e+091213122018-12-13 12:50:15America/New_YorkBack BayHaymarket SquareUber...0.0001154472040018.11154468800033.51154473120014.08154468800032.841544731200
675706eaaadef4-1396-4ba6-a0ca-53e75bdfd0771.543632e+0921122018-12-01 02:42:59America/New_YorkNortheastern UniversityNorth StationLyft...0.0004154359360028.64154357560042.57154360080027.20154356840040.511543611600
238737a28f68a0-5f2d-4dd2-b2a9-5d90858736c51.543723e+0942122018-12-02 04:03:03America/New_YorkFinancial DistrictHaymarket SquareLyft...0.0000154368360031.55154365840044.72154369080027.95154365840044.051543690800
5569692c54f601-0116-4feb-a04e-0440829491751.543246e+091526112018-11-26 15:23:09America/New_YorkNorth EndFinancial DistrictUber...0.1234154325160040.74154323360046.27154325520037.46154329120043.781543244400
188340ada64d05-007e-4490-96d9-cb26165fc1771.545010e+09117122018-12-17 01:25:08America/New_YorkBoston UniversityBeacon HillUber...0.1261154497960039.07154495440043.70154499040033.64154501920038.291544986800
2266629e6f172e-ff40-478a-9b80-6a90156f87f51.543276e+092326112018-11-26 23:45:14America/New_YorkNorth EndBeacon HillUber...0.1225154325160040.45154323360046.49154325520037.17154329120043.841543244400
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10 rows × 57 columns

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" ], "text/plain": [ " id timestamp hour day month \\\n", "418498 66a79fdc-1595-42b3-b28f-285aaebfcaec 1.544883e+09 14 15 12 \n", "599390 cf48b3da-90d2-4bdd-a3f9-a0aa132ec165 1.544912e+09 22 15 12 \n", "209422 ea45cca5-81f9-47c5-93ec-e7d82e68a9f6 1.543492e+09 11 29 11 \n", "262561 352a8c51-907c-46bc-b622-85357609c988 1.543778e+09 19 2 12 \n", "108636 68581606-3daf-46ac-a09f-cac09a9f76dc 1.544705e+09 12 13 12 \n", "675706 eaaadef4-1396-4ba6-a0ca-53e75bdfd077 1.543632e+09 2 1 12 \n", "238737 a28f68a0-5f2d-4dd2-b2a9-5d90858736c5 1.543723e+09 4 2 12 \n", "556969 2c54f601-0116-4feb-a04e-044082949175 1.543246e+09 15 26 11 \n", "188340 ada64d05-007e-4490-96d9-cb26165fc177 1.545010e+09 1 17 12 \n", "226662 9e6f172e-ff40-478a-9b80-6a90156f87f5 1.543276e+09 23 26 11 \n", "\n", " datetime timezone source \\\n", "418498 2018-12-15 14:10:03 America/New_York Theatre District \n", "599390 2018-12-15 22:15:10 America/New_York Financial District \n", "209422 2018-11-29 11:42:56 America/New_York North End \n", "262561 2018-12-02 19:17:57 America/New_York South Station \n", "108636 2018-12-13 12:50:15 America/New_York Back Bay \n", "675706 2018-12-01 02:42:59 America/New_York Northeastern University \n", "238737 2018-12-02 04:03:03 America/New_York Financial District \n", "556969 2018-11-26 15:23:09 America/New_York North End \n", "188340 2018-12-17 01:25:08 America/New_York Boston University \n", "226662 2018-11-26 23:45:14 America/New_York North End \n", "\n", " destination cab_type ... precipIntensityMax uvIndexTime \\\n", "418498 Boston University Lyft ... 0.0074 1544893200 \n", "599390 North End Uber ... 0.0074 1544893200 \n", "209422 North Station Lyft ... 0.0000 1543507200 \n", "262561 North Station Uber ... 0.0916 1543770000 \n", "108636 Haymarket Square Uber ... 0.0001 1544720400 \n", "675706 North Station Lyft ... 0.0004 1543593600 \n", "238737 Haymarket Square Lyft ... 0.0000 1543683600 \n", "556969 Financial District Uber ... 0.1234 1543251600 \n", "188340 Beacon Hill Uber ... 0.1261 1544979600 \n", "226662 Beacon Hill Uber ... 0.1225 1543251600 \n", "\n", " temperatureMin temperatureMinTime temperatureMax \\\n", "418498 39.54 1544929200 54.47 \n", "599390 39.48 1544929200 54.47 \n", "209422 34.67 1543550400 45.03 \n", "262561 36.32 1543726800 50.80 \n", "108636 18.11 1544688000 33.51 \n", "675706 28.64 1543575600 42.57 \n", "238737 31.55 1543658400 44.72 \n", "556969 40.74 1543233600 46.27 \n", "188340 39.07 1544954400 43.70 \n", "226662 40.45 1543233600 46.49 \n", "\n", " temperatureMaxTime apparentTemperatureMin \\\n", "418498 1544896800 36.46 \n", "599390 1544896800 36.40 \n", "209422 1543510800 30.30 \n", "262561 1543788000 35.84 \n", "108636 1544731200 14.08 \n", "675706 1543600800 27.20 \n", "238737 1543690800 27.95 \n", "556969 1543255200 37.46 \n", "188340 1544990400 33.64 \n", "226662 1543255200 37.17 \n", "\n", " apparentTemperatureMinTime apparentTemperatureMax \\\n", "418498 1544929200 53.80 \n", "599390 1544929200 53.80 \n", "209422 1543550400 38.53 \n", "262561 1543748400 50.13 \n", "108636 1544688000 32.84 \n", "675706 1543568400 40.51 \n", "238737 1543658400 44.05 \n", "556969 1543291200 43.78 \n", "188340 1545019200 38.29 \n", "226662 1543291200 43.84 \n", "\n", " apparentTemperatureMaxTime \n", "418498 1544896800 \n", "599390 1544896800 \n", "209422 1543510800 \n", "262561 1543788000 \n", "108636 1544731200 \n", "675706 1543611600 \n", "238737 1543690800 \n", "556969 1543244400 \n", "188340 1544986800 \n", "226662 1543244400 \n", "\n", "[10 rows x 57 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_inf = data.sample(10, random_state=30)\n", "data_inf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Remove Inference-Set from Dataset" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idtimestamphourdaymonthdatetimetimezonesourcedestinationcab_type...precipIntensityMaxuvIndexTimetemperatureMintemperatureMinTimetemperatureMaxtemperatureMaxTimeapparentTemperatureMinapparentTemperatureMinTimeapparentTemperatureMaxapparentTemperatureMaxTime
0424553bb-7174-41ea-aeb4-fe06d4f4b9d71.544953e+09916122018-12-16 09:30:07America/New_YorkHaymarket SquareNorth StationLyft...0.1276154497960039.89154501200043.68154496880033.73154501200038.071544958000
14bd23055-6827-41c6-b23b-3c491f24e74d1.543284e+09227112018-11-27 02:00:23America/New_YorkHaymarket SquareNorth StationLyft...0.1300154325160040.49154323360047.30154325160036.20154329120043.921543251600
2981a3613-77af-4620-a42a-0c0866077d1e1.543367e+09128112018-11-28 01:00:22America/New_YorkHaymarket SquareNorth StationLyft...0.1064154333800035.36154337760047.55154332000031.04154337760044.121543320000
3c2d88af2-d278-4bfd-a8d0-29ca77cc55121.543554e+09430112018-11-30 04:53:02America/New_YorkHaymarket SquareNorth StationLyft...0.0000154350720034.67154355040045.03154351080030.30154355040038.531543510800
4e0126e1f-8ca9-4f2e-82b3-50505a09db9a1.543463e+09329112018-11-29 03:49:20America/New_YorkHaymarket SquareNorth StationLyft...0.0001154342080033.10154340280042.18154342080029.11154339200035.751543420800
..................................................................
693066616d3611-1820-450a-9845-a9ff304a48421.543708e+09231122018-12-01 23:53:05America/New_YorkWest EndNorth EndUber...0.0000154368360031.42154365840044.76154369080027.77154365840044.091543690800
693067633a3fc3-1f86-4b9e-9d48-2b71321123411.543708e+09231122018-12-01 23:53:05America/New_YorkWest EndNorth EndUber...0.0000154368360031.42154365840044.76154369080027.77154365840044.091543690800
69306864d451d0-639f-47a4-9b7c-6fd92fbd264f1.543708e+09231122018-12-01 23:53:05America/New_YorkWest EndNorth EndUber...0.0000154368360031.42154365840044.76154369080027.77154365840044.091543690800
693069727e5f07-a96b-4ad1-a2c7-9abc3ad55b4e1.543708e+09231122018-12-01 23:53:05America/New_YorkWest EndNorth EndUber...0.0000154368360031.42154365840044.76154369080027.77154365840044.091543690800
693070e7fdc087-fe86-40a5-a3c3-3b2a8badcbda1.543708e+09231122018-12-01 23:53:05America/New_YorkWest EndNorth EndUber...0.0000154368360031.42154365840044.76154369080027.77154365840044.091543690800
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693061 rows × 57 columns

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" ], "text/plain": [ " id timestamp hour day month \\\n", "0 424553bb-7174-41ea-aeb4-fe06d4f4b9d7 1.544953e+09 9 16 12 \n", "1 4bd23055-6827-41c6-b23b-3c491f24e74d 1.543284e+09 2 27 11 \n", "2 981a3613-77af-4620-a42a-0c0866077d1e 1.543367e+09 1 28 11 \n", "3 c2d88af2-d278-4bfd-a8d0-29ca77cc5512 1.543554e+09 4 30 11 \n", "4 e0126e1f-8ca9-4f2e-82b3-50505a09db9a 1.543463e+09 3 29 11 \n", "... ... ... ... ... ... \n", "693066 616d3611-1820-450a-9845-a9ff304a4842 1.543708e+09 23 1 12 \n", "693067 633a3fc3-1f86-4b9e-9d48-2b7132112341 1.543708e+09 23 1 12 \n", "693068 64d451d0-639f-47a4-9b7c-6fd92fbd264f 1.543708e+09 23 1 12 \n", "693069 727e5f07-a96b-4ad1-a2c7-9abc3ad55b4e 1.543708e+09 23 1 12 \n", "693070 e7fdc087-fe86-40a5-a3c3-3b2a8badcbda 1.543708e+09 23 1 12 \n", "\n", " datetime timezone source \\\n", "0 2018-12-16 09:30:07 America/New_York Haymarket Square \n", "1 2018-11-27 02:00:23 America/New_York Haymarket Square \n", "2 2018-11-28 01:00:22 America/New_York Haymarket Square \n", "3 2018-11-30 04:53:02 America/New_York Haymarket Square \n", "4 2018-11-29 03:49:20 America/New_York Haymarket Square \n", "... ... ... ... \n", "693066 2018-12-01 23:53:05 America/New_York West End \n", "693067 2018-12-01 23:53:05 America/New_York West End \n", "693068 2018-12-01 23:53:05 America/New_York West End \n", "693069 2018-12-01 23:53:05 America/New_York West End \n", "693070 2018-12-01 23:53:05 America/New_York West End \n", "\n", " destination cab_type ... precipIntensityMax uvIndexTime \\\n", "0 North Station Lyft ... 0.1276 1544979600 \n", "1 North Station Lyft ... 0.1300 1543251600 \n", "2 North Station Lyft ... 0.1064 1543338000 \n", "3 North Station Lyft ... 0.0000 1543507200 \n", "4 North Station Lyft ... 0.0001 1543420800 \n", "... ... ... ... ... ... \n", "693066 North End Uber ... 0.0000 1543683600 \n", "693067 North End Uber ... 0.0000 1543683600 \n", "693068 North End Uber ... 0.0000 1543683600 \n", "693069 North End Uber ... 0.0000 1543683600 \n", "693070 North End Uber ... 0.0000 1543683600 \n", "\n", " temperatureMin temperatureMinTime temperatureMax \\\n", "0 39.89 1545012000 43.68 \n", "1 40.49 1543233600 47.30 \n", "2 35.36 1543377600 47.55 \n", "3 34.67 1543550400 45.03 \n", "4 33.10 1543402800 42.18 \n", "... ... ... ... \n", "693066 31.42 1543658400 44.76 \n", "693067 31.42 1543658400 44.76 \n", "693068 31.42 1543658400 44.76 \n", "693069 31.42 1543658400 44.76 \n", "693070 31.42 1543658400 44.76 \n", "\n", " temperatureMaxTime apparentTemperatureMin \\\n", "0 1544968800 33.73 \n", "1 1543251600 36.20 \n", "2 1543320000 31.04 \n", "3 1543510800 30.30 \n", "4 1543420800 29.11 \n", "... ... ... \n", "693066 1543690800 27.77 \n", "693067 1543690800 27.77 \n", "693068 1543690800 27.77 \n", "693069 1543690800 27.77 \n", "693070 1543690800 27.77 \n", "\n", " apparentTemperatureMinTime apparentTemperatureMax \\\n", "0 1545012000 38.07 \n", "1 1543291200 43.92 \n", "2 1543377600 44.12 \n", "3 1543550400 38.53 \n", "4 1543392000 35.75 \n", "... ... ... \n", "693066 1543658400 44.09 \n", "693067 1543658400 44.09 \n", "693068 1543658400 44.09 \n", "693069 1543658400 44.09 \n", "693070 1543658400 44.09 \n", "\n", " apparentTemperatureMaxTime \n", "0 1544958000 \n", "1 1543251600 \n", "2 1543320000 \n", "3 1543510800 \n", "4 1543420800 \n", "... ... \n", "693066 1543690800 \n", "693067 1543690800 \n", "693068 1543690800 \n", "693069 1543690800 \n", "693070 1543690800 \n", "\n", "[693061 rows x 57 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#### Remove Inference-Set from Dataset\n", "data_train_test = data.drop(data_inf.index)\n", "data_train_test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Reset Index" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idtimestamphourdaymonthdatetimetimezonesourcedestinationcab_type...precipIntensityMaxuvIndexTimetemperatureMintemperatureMinTimetemperatureMaxtemperatureMaxTimeapparentTemperatureMinapparentTemperatureMinTimeapparentTemperatureMaxapparentTemperatureMaxTime
066a79fdc-1595-42b3-b28f-285aaebfcaec1.544883e+091415122018-12-15 14:10:03America/New_YorkTheatre DistrictBoston UniversityLyft...0.0074154489320039.54154492920054.47154489680036.46154492920053.801544896800
1cf48b3da-90d2-4bdd-a3f9-a0aa132ec1651.544912e+092215122018-12-15 22:15:10America/New_YorkFinancial DistrictNorth EndUber...0.0074154489320039.48154492920054.47154489680036.40154492920053.801544896800
2ea45cca5-81f9-47c5-93ec-e7d82e68a9f61.543492e+091129112018-11-29 11:42:56America/New_YorkNorth EndNorth StationLyft...0.0000154350720034.67154355040045.03154351080030.30154355040038.531543510800
3352a8c51-907c-46bc-b622-85357609c9881.543778e+09192122018-12-02 19:17:57America/New_YorkSouth StationNorth StationUber...0.0916154377000036.32154372680050.80154378800035.84154374840050.131543788000
468581606-3daf-46ac-a09f-cac09a9f76dc1.544705e+091213122018-12-13 12:50:15America/New_YorkBack BayHaymarket SquareUber...0.0001154472040018.11154468800033.51154473120014.08154468800032.841544731200
5eaaadef4-1396-4ba6-a0ca-53e75bdfd0771.543632e+0921122018-12-01 02:42:59America/New_YorkNortheastern UniversityNorth StationLyft...0.0004154359360028.64154357560042.57154360080027.20154356840040.511543611600
6a28f68a0-5f2d-4dd2-b2a9-5d90858736c51.543723e+0942122018-12-02 04:03:03America/New_YorkFinancial DistrictHaymarket SquareLyft...0.0000154368360031.55154365840044.72154369080027.95154365840044.051543690800
72c54f601-0116-4feb-a04e-0440829491751.543246e+091526112018-11-26 15:23:09America/New_YorkNorth EndFinancial DistrictUber...0.1234154325160040.74154323360046.27154325520037.46154329120043.781543244400
8ada64d05-007e-4490-96d9-cb26165fc1771.545010e+09117122018-12-17 01:25:08America/New_YorkBoston UniversityBeacon HillUber...0.1261154497960039.07154495440043.70154499040033.64154501920038.291544986800
99e6f172e-ff40-478a-9b80-6a90156f87f51.543276e+092326112018-11-26 23:45:14America/New_YorkNorth EndBeacon HillUber...0.1225154325160040.45154323360046.49154325520037.17154329120043.841543244400
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10 rows × 57 columns

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" ], "text/plain": [ " id timestamp hour day month \\\n", "0 66a79fdc-1595-42b3-b28f-285aaebfcaec 1.544883e+09 14 15 12 \n", "1 cf48b3da-90d2-4bdd-a3f9-a0aa132ec165 1.544912e+09 22 15 12 \n", "2 ea45cca5-81f9-47c5-93ec-e7d82e68a9f6 1.543492e+09 11 29 11 \n", "3 352a8c51-907c-46bc-b622-85357609c988 1.543778e+09 19 2 12 \n", "4 68581606-3daf-46ac-a09f-cac09a9f76dc 1.544705e+09 12 13 12 \n", "5 eaaadef4-1396-4ba6-a0ca-53e75bdfd077 1.543632e+09 2 1 12 \n", "6 a28f68a0-5f2d-4dd2-b2a9-5d90858736c5 1.543723e+09 4 2 12 \n", "7 2c54f601-0116-4feb-a04e-044082949175 1.543246e+09 15 26 11 \n", "8 ada64d05-007e-4490-96d9-cb26165fc177 1.545010e+09 1 17 12 \n", "9 9e6f172e-ff40-478a-9b80-6a90156f87f5 1.543276e+09 23 26 11 \n", "\n", " datetime timezone source \\\n", "0 2018-12-15 14:10:03 America/New_York Theatre District \n", "1 2018-12-15 22:15:10 America/New_York Financial District \n", "2 2018-11-29 11:42:56 America/New_York North End \n", "3 2018-12-02 19:17:57 America/New_York South Station \n", "4 2018-12-13 12:50:15 America/New_York Back Bay \n", "5 2018-12-01 02:42:59 America/New_York Northeastern University \n", "6 2018-12-02 04:03:03 America/New_York Financial District \n", "7 2018-11-26 15:23:09 America/New_York North End \n", "8 2018-12-17 01:25:08 America/New_York Boston University \n", "9 2018-11-26 23:45:14 America/New_York North End \n", "\n", " destination cab_type ... precipIntensityMax uvIndexTime \\\n", "0 Boston University Lyft ... 0.0074 1544893200 \n", "1 North End Uber ... 0.0074 1544893200 \n", "2 North Station Lyft ... 0.0000 1543507200 \n", "3 North Station Uber ... 0.0916 1543770000 \n", "4 Haymarket Square Uber ... 0.0001 1544720400 \n", "5 North Station Lyft ... 0.0004 1543593600 \n", "6 Haymarket Square Lyft ... 0.0000 1543683600 \n", "7 Financial District Uber ... 0.1234 1543251600 \n", "8 Beacon Hill Uber ... 0.1261 1544979600 \n", "9 Beacon Hill Uber ... 0.1225 1543251600 \n", "\n", " temperatureMin temperatureMinTime temperatureMax temperatureMaxTime \\\n", "0 39.54 1544929200 54.47 1544896800 \n", "1 39.48 1544929200 54.47 1544896800 \n", "2 34.67 1543550400 45.03 1543510800 \n", "3 36.32 1543726800 50.80 1543788000 \n", "4 18.11 1544688000 33.51 1544731200 \n", "5 28.64 1543575600 42.57 1543600800 \n", "6 31.55 1543658400 44.72 1543690800 \n", "7 40.74 1543233600 46.27 1543255200 \n", "8 39.07 1544954400 43.70 1544990400 \n", "9 40.45 1543233600 46.49 1543255200 \n", "\n", " apparentTemperatureMin apparentTemperatureMinTime apparentTemperatureMax \\\n", "0 36.46 1544929200 53.80 \n", "1 36.40 1544929200 53.80 \n", "2 30.30 1543550400 38.53 \n", "3 35.84 1543748400 50.13 \n", "4 14.08 1544688000 32.84 \n", "5 27.20 1543568400 40.51 \n", "6 27.95 1543658400 44.05 \n", "7 37.46 1543291200 43.78 \n", "8 33.64 1545019200 38.29 \n", "9 37.17 1543291200 43.84 \n", "\n", " apparentTemperatureMaxTime \n", "0 1544896800 \n", "1 1544896800 \n", "2 1543510800 \n", "3 1543788000 \n", "4 1544731200 \n", "5 1543611600 \n", "6 1543690800 \n", "7 1543244400 \n", "8 1544986800 \n", "9 1543244400 \n", "\n", "[10 rows x 57 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#reset index\n", "data_train_test.reset_index(drop=True, inplace=True)\n", "data_inf.reset_index(drop=True, inplace=True)\n", "data_inf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "alasannya di reset index karena ketika data inference diambil ada index menjadi tidak beraturan oleh karena itu lakukan reset index agar index tidak ada missing value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Splitting Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Membuat training set dan test set dengan perbandingan **80:20**, dimana:\n", "- **80%** data menjadi **training set**\n", "- **20%** data menjadi **test set**" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idtimestamphourdaymonthdatetimetimezonesourcedestinationcab_type...precipIntensityMaxuvIndexTimetemperatureMintemperatureMinTimetemperatureMaxtemperatureMaxTimeapparentTemperatureMinapparentTemperatureMinTimeapparentTemperatureMaxapparentTemperatureMaxTime
0424553bb-7174-41ea-aeb4-fe06d4f4b9d71.544953e+09916122018-12-16 09:30:07America/New_YorkHaymarket SquareNorth StationLyft...0.1276154497960039.89154501200043.68154496880033.73154501200038.071544958000
14bd23055-6827-41c6-b23b-3c491f24e74d1.543284e+09227112018-11-27 02:00:23America/New_YorkHaymarket SquareNorth StationLyft...0.1300154325160040.49154323360047.30154325160036.20154329120043.921543251600
2981a3613-77af-4620-a42a-0c0866077d1e1.543367e+09128112018-11-28 01:00:22America/New_YorkHaymarket SquareNorth StationLyft...0.1064154333800035.36154337760047.55154332000031.04154337760044.121543320000
3c2d88af2-d278-4bfd-a8d0-29ca77cc55121.543554e+09430112018-11-30 04:53:02America/New_YorkHaymarket SquareNorth StationLyft...0.0000154350720034.67154355040045.03154351080030.30154355040038.531543510800
4e0126e1f-8ca9-4f2e-82b3-50505a09db9a1.543463e+09329112018-11-29 03:49:20America/New_YorkHaymarket SquareNorth StationLyft...0.0001154342080033.10154340280042.18154342080029.11154339200035.751543420800
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693056616d3611-1820-450a-9845-a9ff304a48421.543708e+09231122018-12-01 23:53:05America/New_YorkWest EndNorth EndUber...0.0000154368360031.42154365840044.76154369080027.77154365840044.091543690800
693057633a3fc3-1f86-4b9e-9d48-2b71321123411.543708e+09231122018-12-01 23:53:05America/New_YorkWest EndNorth EndUber...0.0000154368360031.42154365840044.76154369080027.77154365840044.091543690800
69305864d451d0-639f-47a4-9b7c-6fd92fbd264f1.543708e+09231122018-12-01 23:53:05America/New_YorkWest EndNorth EndUber...0.0000154368360031.42154365840044.76154369080027.77154365840044.091543690800
693059727e5f07-a96b-4ad1-a2c7-9abc3ad55b4e1.543708e+09231122018-12-01 23:53:05America/New_YorkWest EndNorth EndUber...0.0000154368360031.42154365840044.76154369080027.77154365840044.091543690800
693060e7fdc087-fe86-40a5-a3c3-3b2a8badcbda1.543708e+09231122018-12-01 23:53:05America/New_YorkWest EndNorth EndUber...0.0000154368360031.42154365840044.76154369080027.77154365840044.091543690800
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693061 rows × 56 columns

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" ], "text/plain": [ " id timestamp hour day month \\\n", "0 424553bb-7174-41ea-aeb4-fe06d4f4b9d7 1.544953e+09 9 16 12 \n", "1 4bd23055-6827-41c6-b23b-3c491f24e74d 1.543284e+09 2 27 11 \n", "2 981a3613-77af-4620-a42a-0c0866077d1e 1.543367e+09 1 28 11 \n", "3 c2d88af2-d278-4bfd-a8d0-29ca77cc5512 1.543554e+09 4 30 11 \n", "4 e0126e1f-8ca9-4f2e-82b3-50505a09db9a 1.543463e+09 3 29 11 \n", "... ... ... ... ... ... \n", "693056 616d3611-1820-450a-9845-a9ff304a4842 1.543708e+09 23 1 12 \n", "693057 633a3fc3-1f86-4b9e-9d48-2b7132112341 1.543708e+09 23 1 12 \n", "693058 64d451d0-639f-47a4-9b7c-6fd92fbd264f 1.543708e+09 23 1 12 \n", "693059 727e5f07-a96b-4ad1-a2c7-9abc3ad55b4e 1.543708e+09 23 1 12 \n", "693060 e7fdc087-fe86-40a5-a3c3-3b2a8badcbda 1.543708e+09 23 1 12 \n", "\n", " datetime timezone source \\\n", "0 2018-12-16 09:30:07 America/New_York Haymarket Square \n", "1 2018-11-27 02:00:23 America/New_York Haymarket Square \n", "2 2018-11-28 01:00:22 America/New_York Haymarket Square \n", "3 2018-11-30 04:53:02 America/New_York Haymarket Square \n", "4 2018-11-29 03:49:20 America/New_York Haymarket Square \n", "... ... ... ... \n", "693056 2018-12-01 23:53:05 America/New_York West End \n", "693057 2018-12-01 23:53:05 America/New_York West End \n", "693058 2018-12-01 23:53:05 America/New_York West End \n", "693059 2018-12-01 23:53:05 America/New_York West End \n", "693060 2018-12-01 23:53:05 America/New_York West End \n", "\n", " destination cab_type ... precipIntensityMax uvIndexTime \\\n", "0 North Station Lyft ... 0.1276 1544979600 \n", "1 North Station Lyft ... 0.1300 1543251600 \n", "2 North Station Lyft ... 0.1064 1543338000 \n", "3 North Station Lyft ... 0.0000 1543507200 \n", "4 North Station Lyft ... 0.0001 1543420800 \n", "... ... ... ... ... ... \n", "693056 North End Uber ... 0.0000 1543683600 \n", "693057 North End Uber ... 0.0000 1543683600 \n", "693058 North End Uber ... 0.0000 1543683600 \n", "693059 North End Uber ... 0.0000 1543683600 \n", "693060 North End Uber ... 0.0000 1543683600 \n", "\n", " temperatureMin temperatureMinTime temperatureMax \\\n", "0 39.89 1545012000 43.68 \n", "1 40.49 1543233600 47.30 \n", "2 35.36 1543377600 47.55 \n", "3 34.67 1543550400 45.03 \n", "4 33.10 1543402800 42.18 \n", "... ... ... ... \n", "693056 31.42 1543658400 44.76 \n", "693057 31.42 1543658400 44.76 \n", "693058 31.42 1543658400 44.76 \n", "693059 31.42 1543658400 44.76 \n", "693060 31.42 1543658400 44.76 \n", "\n", " temperatureMaxTime apparentTemperatureMin \\\n", "0 1544968800 33.73 \n", "1 1543251600 36.20 \n", "2 1543320000 31.04 \n", "3 1543510800 30.30 \n", "4 1543420800 29.11 \n", "... ... ... \n", "693056 1543690800 27.77 \n", "693057 1543690800 27.77 \n", "693058 1543690800 27.77 \n", "693059 1543690800 27.77 \n", "693060 1543690800 27.77 \n", "\n", " apparentTemperatureMinTime apparentTemperatureMax \\\n", "0 1545012000 38.07 \n", "1 1543291200 43.92 \n", "2 1543377600 44.12 \n", "3 1543550400 38.53 \n", "4 1543392000 35.75 \n", "... ... ... \n", "693056 1543658400 44.09 \n", "693057 1543658400 44.09 \n", "693058 1543658400 44.09 \n", "693059 1543658400 44.09 \n", "693060 1543658400 44.09 \n", "\n", " apparentTemperatureMaxTime \n", "0 1544958000 \n", "1 1543251600 \n", "2 1543320000 \n", "3 1543510800 \n", "4 1543420800 \n", "... ... \n", "693056 1543690800 \n", "693057 1543690800 \n", "693058 1543690800 \n", "693059 1543690800 \n", "693060 1543690800 \n", "\n", "[693061 rows x 56 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = data_train_test.drop(['price'], axis=1)\n", "y = data_train_test['price']\n", "\n", "X" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "melakukan pemisahan data antara features (X) dan target (y) dari data_train_test" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X Train Size : (554448, 56)\n", "y Test Size : (138613, 56)\n" ] } ], "source": [ "# Split between Train-Set and Test-Set\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)\n", "\n", "print('X Train Size : ', X_train.shape)\n", "print('y Test Size : ', X_test.shape)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Handling Missing Value" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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missing value X_train
id0
timestamp0
hour0
day0
month0
datetime0
timezone0
source0
destination0
cab_type0
product_id0
name0
distance0
surge_multiplier0
latitude0
longitude0
temperature0
apparentTemperature0
short_summary0
long_summary0
precipIntensity0
precipProbability0
humidity0
windSpeed0
windGust0
windGustTime0
visibility0
temperatureHigh0
temperatureHighTime0
temperatureLow0
temperatureLowTime0
apparentTemperatureHigh0
apparentTemperatureHighTime0
apparentTemperatureLow0
apparentTemperatureLowTime0
icon0
dewPoint0
pressure0
windBearing0
cloudCover0
uvIndex0
visibility.10
ozone0
sunriseTime0
sunsetTime0
moonPhase0
precipIntensityMax0
uvIndexTime0
temperatureMin0
temperatureMinTime0
temperatureMax0
temperatureMaxTime0
apparentTemperatureMin0
apparentTemperatureMinTime0
apparentTemperatureMax0
apparentTemperatureMaxTime0
\n", "
" ], "text/plain": [ " missing value X_train\n", "id 0\n", "timestamp 0\n", "hour 0\n", "day 0\n", "month 0\n", "datetime 0\n", "timezone 0\n", "source 0\n", "destination 0\n", "cab_type 0\n", "product_id 0\n", "name 0\n", "distance 0\n", "surge_multiplier 0\n", "latitude 0\n", "longitude 0\n", "temperature 0\n", "apparentTemperature 0\n", "short_summary 0\n", "long_summary 0\n", "precipIntensity 0\n", "precipProbability 0\n", "humidity 0\n", "windSpeed 0\n", "windGust 0\n", "windGustTime 0\n", "visibility 0\n", "temperatureHigh 0\n", "temperatureHighTime 0\n", "temperatureLow 0\n", "temperatureLowTime 0\n", "apparentTemperatureHigh 0\n", "apparentTemperatureHighTime 0\n", "apparentTemperatureLow 0\n", "apparentTemperatureLowTime 0\n", "icon 0\n", "dewPoint 0\n", "pressure 0\n", "windBearing 0\n", "cloudCover 0\n", "uvIndex 0\n", "visibility.1 0\n", "ozone 0\n", "sunriseTime 0\n", "sunsetTime 0\n", "moonPhase 0\n", "precipIntensityMax 0\n", "uvIndexTime 0\n", "temperatureMin 0\n", "temperatureMinTime 0\n", "temperatureMax 0\n", "temperatureMaxTime 0\n", "apparentTemperatureMin 0\n", "apparentTemperatureMinTime 0\n", "apparentTemperatureMax 0\n", "apparentTemperatureMaxTime 0" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#cek X_train missing value\n", "pd.DataFrame({'missing value X_train': X_train.isna().sum()})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tidak ada missing value di X_train" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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missing value X_test
id0
timestamp0
hour0
day0
month0
datetime0
timezone0
source0
destination0
cab_type0
product_id0
name0
distance0
surge_multiplier0
latitude0
longitude0
temperature0
apparentTemperature0
short_summary0
long_summary0
precipIntensity0
precipProbability0
humidity0
windSpeed0
windGust0
windGustTime0
visibility0
temperatureHigh0
temperatureHighTime0
temperatureLow0
temperatureLowTime0
apparentTemperatureHigh0
apparentTemperatureHighTime0
apparentTemperatureLow0
apparentTemperatureLowTime0
icon0
dewPoint0
pressure0
windBearing0
cloudCover0
uvIndex0
visibility.10
ozone0
sunriseTime0
sunsetTime0
moonPhase0
precipIntensityMax0
uvIndexTime0
temperatureMin0
temperatureMinTime0
temperatureMax0
temperatureMaxTime0
apparentTemperatureMin0
apparentTemperatureMinTime0
apparentTemperatureMax0
apparentTemperatureMaxTime0
\n", "
" ], "text/plain": [ " missing value X_test\n", "id 0\n", "timestamp 0\n", "hour 0\n", "day 0\n", "month 0\n", "datetime 0\n", "timezone 0\n", "source 0\n", "destination 0\n", "cab_type 0\n", "product_id 0\n", "name 0\n", "distance 0\n", "surge_multiplier 0\n", "latitude 0\n", "longitude 0\n", "temperature 0\n", "apparentTemperature 0\n", "short_summary 0\n", "long_summary 0\n", "precipIntensity 0\n", "precipProbability 0\n", "humidity 0\n", "windSpeed 0\n", "windGust 0\n", "windGustTime 0\n", "visibility 0\n", "temperatureHigh 0\n", "temperatureHighTime 0\n", "temperatureLow 0\n", "temperatureLowTime 0\n", "apparentTemperatureHigh 0\n", "apparentTemperatureHighTime 0\n", "apparentTemperatureLow 0\n", "apparentTemperatureLowTime 0\n", "icon 0\n", "dewPoint 0\n", "pressure 0\n", "windBearing 0\n", "cloudCover 0\n", "uvIndex 0\n", "visibility.1 0\n", "ozone 0\n", "sunriseTime 0\n", "sunsetTime 0\n", "moonPhase 0\n", "precipIntensityMax 0\n", "uvIndexTime 0\n", "temperatureMin 0\n", "temperatureMinTime 0\n", "temperatureMax 0\n", "temperatureMaxTime 0\n", "apparentTemperatureMin 0\n", "apparentTemperatureMinTime 0\n", "apparentTemperatureMax 0\n", "apparentTemperatureMaxTime 0" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#cek X_test missing value\n", "pd.DataFrame({'missing value X_test': X_test.isna().sum()})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tidak ada missing value di X_test" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Missing value y_train : 44240\n" ] } ], "source": [ "#cek y_train missing value\n", "print('Missing value y_train :' , y_train.isna().sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ada 44065 baris missing value di y_train dan hapus missing valuenya" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Missing value y_test : 10855\n" ] } ], "source": [ "print('Missing value y_test :' , y_test.isna().sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ada 11082 baris missing value di y_train dan hapus missing valuenya" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13.5" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop kolom yang mempunya missing value di y train\n", "y_train.median()\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13.5" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_test.median()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "# membuat fungsi untuk imputasi na\n", "def impute(df, median_value):\n", " df = df.fillna(median_value)\n", " return df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "masukan median value 13,5" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "y_train = impute(y_train, 13.5)\n", "y_test = impute(y_test, 13.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Handling Outlier" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "#buat fungsi tabel untuk X_train\n", "def diagnostic_plots_X(data, variable):\n", " # Define figure size\n", " plt.figure(figsize=(16, 4))\n", "\n", " # Histogram\n", " plt.subplot(1, 2, 1)\n", " sns.histplot(data[variable], bins=30)\n", " plt.title('Histogram')\n", "\n", " # Boxplot\n", " plt.subplot(1, 2, 2)\n", " sns.boxplot(y=data[variable])\n", " plt.title('Boxplot')\n", "\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "#buat fungsi tabel untuk y_train\n", "def diagnostic_plots_y(data):\n", " # Define figure size\n", " plt.figure(figsize=(16, 4))\n", "\n", " # Histogram\n", " plt.subplot(1, 2, 1)\n", " sns.histplot(data, bins=30)\n", " plt.title('Histogram')\n", "\n", " # Boxplot\n", " plt.subplot(1, 2, 2)\n", " sns.boxplot(y=data)\n", " plt.title('Boxplot')\n", "\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# panggil fungsi\n", "\n", "diagnostic_plots_X(X_train, 'distance')\n", "diagnostic_plots_X(X_train, 'surge_multiplier')\n", "diagnostic_plots_y(y_train)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8355506349543478" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train['distance'].skew()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8.292767240231013" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train['surge_multiplier'].skew()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.1568081008627593" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train.skew()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "karena distance, surge multiplier dan price nya adalah positively skewed maka gunakan deteksi outlier dengan metode IQR." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# Fungsi untuk mencari upper dan lower boundaries untuk X_train\n", "\n", "def find_skewed_boundaries_X(df, variable, distance):\n", " IQR = df[variable].quantile(0.75) - df[variable].quantile(0.25)\n", "\n", " lower_boundary_X = df[variable].quantile(0.25) - (IQR * distance)\n", " upper_boundary_X = df[variable].quantile(0.75) + (IQR * distance)\n", "\n", " return upper_boundary_X, lower_boundary_X" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "# Fungsi untuk mencari upper dan lower boundaries untuk y_train\n", "\n", "def find_skewed_boundaries_y(df, distance):\n", " IQR = df.quantile(0.75) - df.quantile(0.25)\n", "\n", " lower_boundary_y = df.quantile(0.25) - (IQR * distance)\n", " upper_boundary_y = df.quantile(0.75) + (IQR * distance)\n", "\n", " return upper_boundary_y, lower_boundary_y" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "distance_upper_limit : 5.405\n", "distance_lower_limit : -1.195 \n", "\n", "surge_multiplier_upper_limit : 1.0\n", "surge_multiplier_lower_limit : 1.0 \n", "\n", "price_upper_limit : 42.0\n", "price_lower_limit : -10.0\n" ] } ], "source": [ "# Limits untuk distance\n", "distance_upper_limit, distance_lower_limit = find_skewed_boundaries_X(X_train, 'distance', 1.5)\n", "distance_upper_limit, distance_lower_limit\n", "\n", "# Limits untuk surge_multiplier\n", "surge_multiplier_upper_limit, surge_multiplier_lower_limit = find_skewed_boundaries_X(X_train,'surge_multiplier', 1.5)\n", "surge_multiplier_upper_limit, surge_multiplier_lower_limit\n", "\n", "# Limits untuk price\n", "price_upper_limit, price_lower_limit = find_skewed_boundaries_y(y_train, 1.5)\n", "price_upper_limit, price_lower_limit\n", "\n", "print('distance_upper_limit : ', distance_upper_limit)\n", "print('distance_lower_limit : ', distance_lower_limit, '\\n')\n", "print('surge_multiplier_upper_limit : ', surge_multiplier_upper_limit)\n", "print('surge_multiplier_lower_limit : ', surge_multiplier_lower_limit, '\\n')\n", "print('price_upper_limit : ', price_upper_limit)\n", "print('price_lower_limit : ', price_lower_limit)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "# Flag the outliers in category distance\n", "outliers_distance = np.where(X_train['distance'] > distance_upper_limit, True,\n", " np.where(X_train['distance'] < distance_lower_limit, True, False))\n", "\n", "# Flag the outliers in category surge_multiplier\n", "outliers_surge_multiplier = np.where(X_train['surge_multiplier'] > surge_multiplier_upper_limit, True,\n", " np.where(X_train['surge_multiplier'] < surge_multiplier_lower_limit, True, False))\n", "\n", "# Flag the outliers in category price\n", "outliers_price = np.where(y_train > price_upper_limit, True,\n", " np.where(y_train < price_lower_limit, True, False))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_train dataset - Before trimming : (554448, 56)\n", "X_train dataset - After trimming : (531024, 56)\n", "\n", "y_train dataset - Before trimming : (554448,)\n", "y_train dataset - After trimming : (547963,)\n" ] } ], "source": [ "# trimm the dataset\n", "\n", "X_train_trimmed = X_train.loc[~(outliers_distance + outliers_surge_multiplier)]\n", "\n", "y_train_trimmed = y_train.loc[~(outliers_price)]\n", "\n", "print('X_train dataset - Before trimming : ', X_train.shape)\n", "print('X_train dataset - After trimming : ', X_train_trimmed.shape)\n", "print('')\n", "print('y_train dataset - Before trimming : ', y_train.shape)\n", "print('y_train dataset - After trimming : ', y_train_trimmed.shape)\n", "\n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.273439529045104" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#berapa persen data yang tertrimming di X_train?\n", "(554448-530754)/554448*100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "sebanyak 4,27% data yang tertrimming di X_train" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8765021412982577" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(510438-505964)/510438*100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "sebanyak 0,87% data yang tertrimming di y_train" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "distance - Before Trimming\n" ] }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Cari Outlier \n", "\n", "print('distance - Before Trimming')\n", "diagnostic_plots_X(X_train, 'distance')\n", "print('\\ndistance - After Trimming')\n", "diagnostic_plots_X(X_train_trimmed, 'distance')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "surge_multiplier - Before Trimming\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "surge_multiplier - After Trimming\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print('surge_multiplier - Before Trimming')\n", "diagnostic_plots_X(X_train, 'surge_multiplier')\n", "print('\\nsurge_multiplier - After Trimming')\n", "diagnostic_plots_X(X_train_trimmed, 'surge_multiplier')" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "surge_multiplier - Before Trimming\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "surge_multiplier - After Trimming\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print('surge_multiplier - Before Trimming')\n", "diagnostic_plots_y(y_train)\n", "print('\\nsurge_multiplier - After Trimming')\n", "diagnostic_plots_y(y_train_trimmed)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bisa dilihat dari 3 visualisasi di atas khususnya di boxplot setelah dilakukan handling outlier \n", "\n", "distance, surge_multiplier dan price sudah tidak memiliki outlier " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feature Selection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pada bagian ini akan dilakukan pemilihan feature (kolom) apa saja yang akan digunakan.\n", "\n", "Berdasarakan hasil EDA, akan diasumsikan bahwa kolom `price` hanya memliki hubungan dengan kolom `distance`,`surge_multiplier` dan `name`. \n", "\n", "`distance`,`surge_multiplier` sebagai numerical column\n", "\n", "`name` sebagai categorical column\n", "\n", "Maka kolom-kolom tersebut yang akan dijadikan feature dengan `price` sebagai targetnya." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numerical Columns : ['distance', 'surge_multiplier']\n", "Categorical Columns : ['name']\n" ] } ], "source": [ "num_columns = X_train[['distance', 'surge_multiplier']].columns.tolist()\n", "cat_columns = X_train[['name']].columns.tolist()\n", "\n", "print('Numerical Columns : ', num_columns)\n", "print('Categorical Columns : ', cat_columns)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "# Split Train-set and Test-set based on column types\n", "\n", "X_train_num = X_train[num_columns]\n", "X_train_cat = X_train[cat_columns]\n", "\n", "X_test_num = X_test[num_columns]\n", "X_test_cat = X_test[cat_columns]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feature Scaling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Feature Scaling menggunakan MinMaxScaler " ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " distance surge_multiplier\n", "300658 0.57 1.0\n", "88166 3.28 1.0\n", "60686 4.98 1.0\n", "81438 2.45 1.0\n", "597420 4.45 1.0" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#cek X_train_num\n", "X_train_num.head()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.07015306, 0. ],\n", " [0.41581633, 0. ],\n", " [0.63265306, 0. ],\n", " ...,\n", " [0.42984694, 0. ],\n", " [0.30102041, 0. ],\n", " [0.19005102, 0. ]])" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Feature scaling using MinMaxScaler, defaultnya jadi range 0 s/d 1\n", "\n", "scaler = MinMaxScaler()\n", "scaler.fit(X_train_num) # .fit hanya milik train\n", "\n", "X_train_num_scaled = scaler.transform(X_train_num)\n", "X_test_num_scaled = scaler.transform(X_test_num)\n", "\n", "X_train_num_scaled" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feature Encoding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Feature Encoding menggunakan OrdinalEncoder " ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[4.],\n", " [6.],\n", " [7.],\n", " ...,\n", " [4.],\n", " [1.],\n", " [2.]])" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "encoder = OrdinalEncoder()\n", "encoder.fit(X_train_cat)\n", "\n", "X_train_cat_encoded = encoder.transform(X_train_cat)\n", "X_test_cat_encoded = encoder.transform(X_test_cat)\n", "\n", "X_train_cat_encoded" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Concate Features" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'np' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32mc:\\Users\\Xyla\\OneDrive\\Documents\\Hacktiv8\\ftds\\ftds13\\Assignments\\p1---ftds013---g1-Xylverize\\h8dsft_P1W1Reg_xyla_ramadhan.ipynb Cell 127\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m X_train_final \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mconcatenate([X_train_num_scaled, X_train_cat_encoded], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[0;32m 2\u001b[0m X_test_final \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mconcatenate([X_test_num_scaled, X_test_cat_encoded], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[0;32m 3\u001b[0m X_train_final\n", "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined" ] } ], "source": [ "X_train_final = np.concatenate([X_train_num_scaled, X_train_cat_encoded], axis=1)\n", "X_test_final = np.concatenate([X_test_num_scaled, X_test_cat_encoded], axis=1)\n", "X_train_final" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " distance surge_multiplier name\n", "0 0.070153 0.0 4.0\n", "1 0.415816 0.0 6.0\n", "2 0.632653 0.0 7.0\n", "3 0.309949 0.0 5.0\n", "4 0.565051 0.0 7.0\n", "... ... ... ...\n", "554443 0.174745 0.0 0.0\n", "554444 0.067602 0.0 10.0\n", "554445 0.429847 0.0 4.0\n", "554446 0.301020 0.0 1.0\n", "554447 0.190051 0.0 2.0\n", "\n", "[554448 rows x 3 columns]" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display as DataFrame\n", "\n", "X_train_final_df = pd.DataFrame(X_train_final, columns=[num_columns+cat_columns])\n", "X_train_final_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## VI. Model Definition" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "yang saya pakai untuk model definition adalah **Linear Regression**\n", "\n", "model_lin_reg adalah model **Linear Regression** biasa.\n", "\n" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "# Training using LinearRegression\n", "\n", "from sklearn.linear_model import LinearRegression\n", "model_lin_reg = LinearRegression()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## VII. Model Training" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "300658 27.5\n", "88166 19.5\n", "60686 9.0\n", "81438 9.0\n", "597420 9.0\n", " ... \n", "496379 16.0\n", "328599 8.0\n", "570508 38.0\n", "572333 34.0\n", "431909 13.5\n", "Name: price, Length: 554448, dtype: float64" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(554448, 3)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train_final.shape" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
LinearRegression()
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" ], "text/plain": [ "LinearRegression()" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Train the model\n", "\n", "model_lin_reg.fit(X_train_final, y_train)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([14.6773506 , 18.91938828, 21.92528674, ..., 21.91027963,\n", " 23.38289623, 19.79708277])" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Predict train-set and test-set\n", "\n", "y_pred_train = model_lin_reg.predict(X_train_final)\n", "y_pred_test = model_lin_reg.predict(X_test_final)\n", "\n", "y_pred_train" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## VIII. Model Evaluation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Evaluation : Linear Regression" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE Train-Set Linear : 5.09377663469663\n", "MAE Test-Set Linear : 5.078820189868427\n", "MSE Train-Set Linear : 41.696439543747395\n", "MSE Test-Set Linear : 41.23054259457264\n", "RMSE Train-Set Linear : 6.457278028995453\n", "RMSE Test-Set Linear : 6.421101353706593\n", "R2 Train-Set Linear : 0.48543867429551313\n", "R2 Test-Set Linear : 0.4808323959058034\n" ] } ], "source": [ "# Evaluate Linear Regression Model\n", "\n", "from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score\n", "\n", "print('MAE Train-Set Linear : ', mean_absolute_error(y_train, y_pred_train))\n", "print('MAE Test-Set Linear : ', mean_absolute_error(y_test, y_pred_test))\n", "\n", "print('MSE Train-Set Linear : ', mean_squared_error(y_train, y_pred_train))\n", "print('MSE Test-Set Linear : ', mean_squared_error(y_test, y_pred_test))\n", "\n", "print('RMSE Train-Set Linear : ', np.sqrt(mean_squared_error(y_train, y_pred_train)))\n", "print('RMSE Test-Set Linear : ', np.sqrt(mean_squared_error(y_test, y_pred_test)))\n", "\n", "print('R2 Train-Set Linear : ', r2_score(y_train, y_pred_train))\n", "print('R2 Test-Set Linear : ', r2_score(y_test, y_pred_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## IX. Model Saving dan Model Inference" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "import pickle\n", "\n", "with open('model_scaler.pkl', 'wb') as file_1: # nama file, wb write binary, as aliasnya\n", " pickle.dump(scaler, file_1) # masukin yang fit dan aliasnya\n", "\n", "with open('model_encoder.pkl', 'wb') as file_2:\n", " pickle.dump(encoder, file_2)\n", "\n", "with open('model_lin_reg.pkl', 'wb') as file_3:\n", " pickle.dump(model_lin_reg, file_3)\n", "\n", "with open('list_num_columns.txt', 'w') as file_4:\n", " file_4.write(str(num_columns))\n", "\n", "with open('list_cat_columns.txt', 'w') as file_5:\n", " file_5.write(str(cat_columns))" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "# Load All Models\n", "\n", "with open('model_scaler.pkl', 'rb') as file_1:\n", " model_scaler = pickle.load(file_1)\n", "\n", "with open('model_encoder.pkl', 'rb') as file_2:\n", " model_encoder = pickle.load(file_2)\n", "\n", "with open('model_lin_reg.pkl', 'rb') as file_3:\n", " model_lin_reg = pickle.load(file_3)\n", "\n", "with open('list_num_columns.txt', 'r') as file_4:\n", " list_num_columns = file_4.read()\n", "\n", "with open('list_cat_columns.txt', 'r') as file_5:\n", " list_cat_columns = file_5.read()" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['distance', 'surge_multiplier']" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert num_columns and cat_columns into list\n", "\n", "import ast\n", "\n", "list_num_columns = ast.literal_eval(list_num_columns)\n", "list_cat_columns = ast.literal_eval(list_cat_columns)\n", "list_num_columns" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " distance surge_multiplier\n", "0 4.77 1.0\n", "1 1.21 1.0\n", "2 1.07 1.0\n", "3 1.84 1.0\n", "4 2.32 1.0\n", "5 3.22 1.0\n", "6 1.22 1.0\n", "7 1.04 1.0\n", "8 2.79 1.0\n", "9 1.46 1.0" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Split between num columns and cat columns\n", "\n", "data_inf_num = data_inf[num_columns]\n", "data_inf_cat = data_inf[cat_columns]\n", "\n", "data_inf_num" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.60586735, 0. ],\n", " [0.15178571, 0. ],\n", " [0.13392857, 0. ],\n", " [0.23214286, 0. ],\n", " [0.29336735, 0. ],\n", " [0.40816327, 0. ],\n", " [0.15306122, 0. ],\n", " [0.13010204, 0. ],\n", " [0.35331633, 0. ],\n", " [0.18367347, 0. ]])" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Feature Scaling and Feature Encoding\n", "\n", "data_inf_num_scaled = model_scaler.transform(data_inf_num)\n", "data_inf_cat_encoded = model_encoder.transform(data_inf_cat)\n", "data_inf_num_scaled" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.60586735, 0. , 2. ],\n", " [ 0.15178571, 0. , 12. ],\n", " [ 0.13392857, 0. , 2. ],\n", " [ 0.23214286, 0. , 10. ],\n", " [ 0.29336735, 0. , 0. ],\n", " [ 0.40816327, 0. , 7. ],\n", " [ 0.15306122, 0. , 6. ],\n", " [ 0.13010204, 0. , 1. ],\n", " [ 0.35331633, 0. , 10. ],\n", " [ 0.18367347, 0. , 0. ]])" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Concate\n", "\n", "data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis=1)\n", "data_inf_final" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([28.15855392, 5.48384311, 18.66854065, 9.80846606, 24.58338204,\n", " 17.41111826, 13.63575927, 19.94597251, 12.24509109, 22.37759517])" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Predict Data Inference\n", "\n", "y_pred_inf = model_lin_reg.predict(data_inf_final)\n", "y_pred_inf" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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price_prediction
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" ], "text/plain": [ " price_prediction\n", "0 28.158554\n", "1 5.483843\n", "2 18.668541\n", "3 9.808466\n", "4 24.583382\n", "5 17.411118\n", "6 13.635759\n", "7 19.945973\n", "8 12.245091\n", "9 22.377595" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert into DataFrame\n", "\n", "y_pred_inf_df = pd.DataFrame(y_pred_inf, columns=['price_prediction'])\n", "y_pred_inf_df" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idtimestamphourdaymonthdatetimetimezonesourcedestinationcab_type...uvIndexTimetemperatureMintemperatureMinTimetemperatureMaxtemperatureMaxTimeapparentTemperatureMinapparentTemperatureMinTimeapparentTemperatureMaxapparentTemperatureMaxTimeprice_prediction
066a79fdc-1595-42b3-b28f-285aaebfcaec1.544883e+091415122018-12-15 14:10:03America/New_YorkTheatre DistrictBoston UniversityLyft...154489320039.54154492920054.47154489680036.46154492920053.80154489680028.158554
1cf48b3da-90d2-4bdd-a3f9-a0aa132ec1651.544912e+092215122018-12-15 22:15:10America/New_YorkFinancial DistrictNorth EndUber...154489320039.48154492920054.47154489680036.40154492920053.8015448968005.483843
2ea45cca5-81f9-47c5-93ec-e7d82e68a9f61.543492e+091129112018-11-29 11:42:56America/New_YorkNorth EndNorth StationLyft...154350720034.67154355040045.03154351080030.30154355040038.53154351080018.668541
3352a8c51-907c-46bc-b622-85357609c9881.543778e+09192122018-12-02 19:17:57America/New_YorkSouth StationNorth StationUber...154377000036.32154372680050.80154378800035.84154374840050.1315437880009.808466
468581606-3daf-46ac-a09f-cac09a9f76dc1.544705e+091213122018-12-13 12:50:15America/New_YorkBack BayHaymarket SquareUber...154472040018.11154468800033.51154473120014.08154468800032.84154473120024.583382
5eaaadef4-1396-4ba6-a0ca-53e75bdfd0771.543632e+0921122018-12-01 02:42:59America/New_YorkNortheastern UniversityNorth StationLyft...154359360028.64154357560042.57154360080027.20154356840040.51154361160017.411118
6a28f68a0-5f2d-4dd2-b2a9-5d90858736c51.543723e+0942122018-12-02 04:03:03America/New_YorkFinancial DistrictHaymarket SquareLyft...154368360031.55154365840044.72154369080027.95154365840044.05154369080013.635759
72c54f601-0116-4feb-a04e-0440829491751.543246e+091526112018-11-26 15:23:09America/New_YorkNorth EndFinancial DistrictUber...154325160040.74154323360046.27154325520037.46154329120043.78154324440019.945973
8ada64d05-007e-4490-96d9-cb26165fc1771.545010e+09117122018-12-17 01:25:08America/New_YorkBoston UniversityBeacon HillUber...154497960039.07154495440043.70154499040033.64154501920038.29154498680012.245091
99e6f172e-ff40-478a-9b80-6a90156f87f51.543276e+092326112018-11-26 23:45:14America/New_YorkNorth EndBeacon HillUber...154325160040.45154323360046.49154325520037.17154329120043.84154324440022.377595
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10 rows × 58 columns

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" ], "text/plain": [ " id timestamp hour day month \\\n", "0 66a79fdc-1595-42b3-b28f-285aaebfcaec 1.544883e+09 14 15 12 \n", "1 cf48b3da-90d2-4bdd-a3f9-a0aa132ec165 1.544912e+09 22 15 12 \n", "2 ea45cca5-81f9-47c5-93ec-e7d82e68a9f6 1.543492e+09 11 29 11 \n", "3 352a8c51-907c-46bc-b622-85357609c988 1.543778e+09 19 2 12 \n", "4 68581606-3daf-46ac-a09f-cac09a9f76dc 1.544705e+09 12 13 12 \n", "5 eaaadef4-1396-4ba6-a0ca-53e75bdfd077 1.543632e+09 2 1 12 \n", "6 a28f68a0-5f2d-4dd2-b2a9-5d90858736c5 1.543723e+09 4 2 12 \n", "7 2c54f601-0116-4feb-a04e-044082949175 1.543246e+09 15 26 11 \n", "8 ada64d05-007e-4490-96d9-cb26165fc177 1.545010e+09 1 17 12 \n", "9 9e6f172e-ff40-478a-9b80-6a90156f87f5 1.543276e+09 23 26 11 \n", "\n", " datetime timezone source \\\n", "0 2018-12-15 14:10:03 America/New_York Theatre District \n", "1 2018-12-15 22:15:10 America/New_York Financial District \n", "2 2018-11-29 11:42:56 America/New_York North End \n", "3 2018-12-02 19:17:57 America/New_York South Station \n", "4 2018-12-13 12:50:15 America/New_York Back Bay \n", "5 2018-12-01 02:42:59 America/New_York Northeastern University \n", "6 2018-12-02 04:03:03 America/New_York Financial District \n", "7 2018-11-26 15:23:09 America/New_York North End \n", "8 2018-12-17 01:25:08 America/New_York Boston University \n", "9 2018-11-26 23:45:14 America/New_York North End \n", "\n", " destination cab_type ... uvIndexTime temperatureMin \\\n", "0 Boston University Lyft ... 1544893200 39.54 \n", "1 North End Uber ... 1544893200 39.48 \n", "2 North Station Lyft ... 1543507200 34.67 \n", "3 North Station Uber ... 1543770000 36.32 \n", "4 Haymarket Square Uber ... 1544720400 18.11 \n", "5 North Station Lyft ... 1543593600 28.64 \n", "6 Haymarket Square Lyft ... 1543683600 31.55 \n", "7 Financial District Uber ... 1543251600 40.74 \n", "8 Beacon Hill Uber ... 1544979600 39.07 \n", "9 Beacon Hill Uber ... 1543251600 40.45 \n", "\n", " temperatureMinTime temperatureMax temperatureMaxTime \\\n", "0 1544929200 54.47 1544896800 \n", "1 1544929200 54.47 1544896800 \n", "2 1543550400 45.03 1543510800 \n", "3 1543726800 50.80 1543788000 \n", "4 1544688000 33.51 1544731200 \n", "5 1543575600 42.57 1543600800 \n", "6 1543658400 44.72 1543690800 \n", "7 1543233600 46.27 1543255200 \n", "8 1544954400 43.70 1544990400 \n", "9 1543233600 46.49 1543255200 \n", "\n", " apparentTemperatureMin apparentTemperatureMinTime apparentTemperatureMax \\\n", "0 36.46 1544929200 53.80 \n", "1 36.40 1544929200 53.80 \n", "2 30.30 1543550400 38.53 \n", "3 35.84 1543748400 50.13 \n", "4 14.08 1544688000 32.84 \n", "5 27.20 1543568400 40.51 \n", "6 27.95 1543658400 44.05 \n", "7 37.46 1543291200 43.78 \n", "8 33.64 1545019200 38.29 \n", "9 37.17 1543291200 43.84 \n", "\n", " apparentTemperatureMaxTime price_prediction \n", "0 1544896800 28.158554 \n", "1 1544896800 5.483843 \n", "2 1543510800 18.668541 \n", "3 1543788000 9.808466 \n", "4 1544731200 24.583382 \n", "5 1543611600 17.411118 \n", "6 1543690800 13.635759 \n", "7 1543244400 19.945973 \n", "8 1544986800 12.245091 \n", "9 1543244400 22.377595 \n", "\n", "[10 rows x 58 columns]" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Merge\n", "pd_inf = pd.concat([data_inf, y_pred_inf_df], axis = 1)\n", "pd_inf" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "hasil = []\n", "\n", "# kolom pts\n", "# kolom opp_pts\n", "\n", "for i in range(0, len(pd_inf)): \n", " selisih = pd_inf.loc[i, 'price'] - pd_inf.loc[i, 'price_prediction']\n", " hasil.append(selisih)\n", "\n", "pd_inf['selisih'] = hasil" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [], "source": [ "pd_inf = pd_inf[['price','price_prediction','selisih']]" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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priceprice_predictionselisih
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" ], "text/plain": [ " price price_prediction selisih\n", "0 27.5 28.158554 -0.658554\n", "1 9.0 5.483843 3.516157\n", "2 13.5 18.668541 -5.168541\n", "3 8.5 9.808466 -1.308466\n", "4 22.5 24.583382 -2.083382\n", "5 7.0 17.411118 -10.411118\n", "6 13.5 13.635759 -0.135759\n", "7 27.5 19.945973 7.554027\n", "8 9.5 12.245091 -2.745091\n", "9 16.0 22.377595 -6.377595" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd_inf.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## XI. Pengambilan Kesimpulan" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Intercept : 18.68418459823475\n", "Slope : [20.10856867 37.19490289 -1.35437791]\n" ] } ], "source": [ "# Get Intercept and Slope\n", "\n", "intercept = model_lin_reg.intercept_\n", "slope = model_lin_reg.coef_\n", "\n", "print('Intercept : ', intercept)\n", "print('Slope : ', slope)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('distance',), ('surge_multiplier',), ('name',)]" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display Column's Name\n", "\n", "X_train_final_df.columns.tolist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Persamaan Regresi\n", "\n", "y = 18.68418459823475 + (20.10856867 x distance) + (37.19490289 x surge_multiplier) + (-1.35437791 x name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Kesimpulan :\n", "\n", "1) Model Machine Learning yang paling bagus untuk prediksi price di dataset ini adalah Linear Regression dengan hasil evaluasi sebagai berikut :\n", "- MAE Train-Set Linear : 5.09377663469663\n", "- MAE Test-Set Linear : 5.078820189868427\n", "- MSE Train-Set Linear : 41.696439543747395\n", "- MSE Test-Set Linear : 41.23054259457264\n", "- RMSE Train-Set Linear : 6.457278028995453\n", "- RMSE Test-Set Linear : 6.421101353706593\n", "- R2 Train-Set Linear : 0.48543867429551313\n", "- R2 Test-Set Linear : 0.4808323959058034\n", "\n", "2) Performa model masih kurang bagus. Sepertinya data ini kurang cocok untuk diterapkan model linear.\n", "\n", "3) Hasil prediksi data inference kurang bagus disebabkan karena performa model juga kurang bagus.\n", "\n", "4) Persamaan regresi yang didapat adalah **y = 18.68418459823475 + (20.10856867 x distance) + (37.19490289 x surge_multiplier) + (-1.35437791 x name)**\n", "\n", "- artinya coefisien fitur tersebut merepresentasikan besarnya pengaruh tiap fitur terhadap hasil prediksi price." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### insight EDA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "5) Dari Visualisasi tersebut yang menarik perhatian saya adalah ada apa dengan tanggal 9 dan 10, yang menyebabkan pemesanan uber dan lyft menurun drastis\n", "\n", " ternyata adanya missing data dari tanggal 5 sampai tanggal 8 dan berpengaruh juga di tanggal 4, 9 dan 10. sehingga terkesan bahwa tanggal 9 dan 10 mengalami penurunan drastis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6) Desember mempunyai lebih banyak data dari November karena range data dari tanggal 11-26-2018 to 12-18-2018. 5 hari di bulan November dan 18 hari di bulan Desember" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "7) Terlihat bahwa uber mendapatkan pemesanan sebanyak 385663 yaitu 55.64% dari total dataset sedangkan Lyft memiliki pemesanan sebanyak 307408 yaitu 44.35% dari total dataset ini yang membuat uber mendominasi pemesanan dari mulai jam,hari dan bulan. yang menariknya adalah tidak ada satu hari pun lyft mengalahkan pemesanan uber" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.10.5 64-bit", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.5 (tags/v3.10.5:f377153, Jun 6 2022, 16:14:13) [MSC v.1929 64 bit (AMD64)]" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "aa24d1753723fb44936862a0479a9c898341c4bbf00849cc75b84e50be8d531a" } } }, "nbformat": 4, "nbformat_minor": 2 }