From 35de4681aa98335afba38ad1f5d2f7f24ecfa645 Mon Sep 17 00:00:00 2001 From: AtharvInamdar <90698481+AtharvInamdar@users.noreply.github.com> Date: Thu, 24 Feb 2022 22:22:39 +0530 Subject: [PATCH 1/2] Add files via upload --- setdataset.ipynb | 2116 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2116 insertions(+) create mode 100644 setdataset.ipynb diff --git a/setdataset.ipynb b/setdataset.ipynb new file mode 100644 index 0000000..db0a3e9 --- /dev/null +++ b/setdataset.ipynb @@ -0,0 +1,2116 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "setdataset.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "cXAaaqUMtXeK" + }, + "outputs": [], + "source": [ + "# Importing the libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error" + ] + }, + { + "cell_type": "code", + "source": [ + "# Importing the dataset\n", + "df=pd.read_csv('day.csv')" + ], + "metadata": { + "id": "ShNt4BLht4Wb" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "id": "dFiagtGFt-5o", + "outputId": "8f76f919-b13e-45ce-9165-d3c56af1cee8" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
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instantdtedayseasonyrmnthholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
012011-01-0110106020.3441670.3636250.8058330.160446331654985
122011-01-0210100020.3634780.3537390.6960870.248539131670801
232011-01-0310101110.1963640.1894050.4372730.24830912012291349
342011-01-0410102110.2000000.2121220.5904350.16029610814541562
452011-01-0510103110.2269570.2292700.4369570.1869008215181600
...................................................
7267272012-12-27111204120.2541670.2266420.6529170.35013324718672114
7277282012-12-28111205120.2533330.2550460.5900000.15547164424513095
7287292012-12-29111206020.2533330.2424000.7529170.12438315911821341
7297302012-12-30111200010.2558330.2317000.4833330.35075436414321796
7307312012-12-31111201120.2158330.2234870.5775000.15484643922902729
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instantdtedayseasonyrmnthholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
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1FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
2FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
3FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
4FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
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730FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
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seasonyrmnthholidayweekdayworkingdayweathersittempatemphumwindspeedcnt
010106020.3441670.3636250.8058330.160446985
110100020.3634780.3537390.6960870.248539801
210101110.1963640.1894050.4372730.2483091349
310102110.2000000.2121220.5904350.1602961562
410103110.2269570.2292700.4369570.1869001600
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726111204120.2541670.2266420.6529170.3501332114
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seasonyrmnthholidayweekdayworkingdayweathersittempatemphumwindspeedcnt
010106020.3441670.3636250.8058330.160446985
110100020.3634780.3537390.6960870.248539801
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726111204120.2541670.2266420.6529170.3501332114
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731 rows × 12 columns

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\n", + " " + ], + "text/plain": [ + " season yr mnth holiday ... atemp hum windspeed cnt\n", + "0 1 0 1 0 ... 0.363625 0.805833 0.160446 985\n", + "1 1 0 1 0 ... 0.353739 0.696087 0.248539 801\n", + "2 1 0 1 0 ... 0.189405 0.437273 0.248309 1349\n", + "3 1 0 1 0 ... 0.212122 0.590435 0.160296 1562\n", + "4 1 0 1 0 ... 0.229270 0.436957 0.186900 1600\n", + ".. ... .. ... ... ... ... ... ... ...\n", + "726 1 1 12 0 ... 0.226642 0.652917 0.350133 2114\n", + "727 1 1 12 0 ... 0.255046 0.590000 0.155471 3095\n", + "728 1 1 12 0 ... 0.242400 0.752917 0.124383 1341\n", + "729 1 1 12 0 ... 0.231700 0.483333 0.350754 1796\n", + "730 1 1 12 0 ... 0.223487 0.577500 0.154846 2729\n", + "\n", + "[731 rows x 12 columns]" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df.columns" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EnW8izPJwlbl", + "outputId": "1dbf3c84-c844-4021-868f-ffa01797308c" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['season', 'yr', 'mnth', 'holiday', 'weekday', 'workingday',\n", + " 'weathersit', 'temp', 'atemp', 'hum', 'windspeed', 'cnt'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ] + }, + { + "cell_type": "code", + "source": [ + "for col in df:\n", + " print(df[col].value_counts(ascending=False), '\\n\\n\\n')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AhKPKfVuwprY", + "outputId": "5a7f7986-98b8-45f1-d5c1-b33d2b5be156" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "3 188\n", + "2 184\n", + "1 181\n", + "4 178\n", + "Name: season, dtype: int64 \n", + "\n", + "\n", + "\n", + "1 366\n", + "0 365\n", + "Name: yr, dtype: int64 \n", + "\n", + "\n", + "\n", + "1 62\n", + "3 62\n", + "5 62\n", + "7 62\n", + "8 62\n", + "10 62\n", + "12 62\n", + "4 60\n", + "6 60\n", + "9 60\n", + "11 60\n", + "2 57\n", + "Name: mnth, dtype: int64 \n", + "\n", + "\n", + "\n", + "0 710\n", + "1 21\n", + "Name: holiday, dtype: int64 \n", + "\n", + "\n", + "\n", + "6 105\n", + "0 105\n", + "1 105\n", + "2 104\n", + "3 104\n", + "4 104\n", + "5 104\n", + "Name: weekday, dtype: int64 \n", + "\n", + "\n", + "\n", + "1 500\n", + "0 231\n", + "Name: workingday, dtype: int64 \n", + "\n", + "\n", + "\n", + "1 463\n", + "2 247\n", + "3 21\n", + "Name: weathersit, dtype: int64 \n", + "\n", + "\n", + "\n", + "0.635000 5\n", + "0.265833 5\n", + "0.680000 4\n", + "0.710833 4\n", + "0.564167 4\n", + " ..\n", + "0.669167 1\n", + "0.643333 1\n", + "0.707059 1\n", + "0.700000 1\n", + "0.215833 1\n", + "Name: temp, Length: 499, dtype: int64 \n", + "\n", + "\n", + "\n", + "0.654688 4\n", + "0.375621 3\n", + "0.637008 3\n", + "0.571975 2\n", + "0.466525 2\n", + " ..\n", + "0.578946 1\n", + "0.609229 1\n", + "0.602130 1\n", + "0.626900 1\n", + "0.223487 1\n", + "Name: atemp, Length: 690, dtype: int64 \n", + "\n", + "\n", + "\n", + "0.613333 4\n", + "0.605000 3\n", + "0.590000 3\n", + "0.538333 3\n", + "0.690000 3\n", + " ..\n", + "0.548333 1\n", + "0.561765 1\n", + "0.850000 1\n", + "0.761250 1\n", + "0.577500 1\n", + "Name: hum, Length: 595, dtype: int64 \n", + "\n", + "\n", + "\n", + "0.134954 3\n", + "0.228858 3\n", + "0.136817 3\n", + "0.110700 3\n", + "0.118792 3\n", + " ..\n", + "0.206467 1\n", + "0.212696 1\n", + "0.343943 1\n", + "0.097021 1\n", + "0.154846 1\n", + "Name: windspeed, Length: 650, dtype: int64 \n", + "\n", + "\n", + "\n", + "5409 2\n", + "2424 2\n", + "5698 2\n", + "4459 2\n", + "5119 2\n", + " ..\n", + "5046 1\n", + "4713 1\n", + "4763 1\n", + "4785 1\n", + "2729 1\n", + "Name: cnt, Length: 696, dtype: int64 \n", + "\n", + "\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Replace negative values in dataframe with 0\n", + "num = df._get_numeric_data()\n", + "num[num < 0] = 0" + ], + "metadata": { + "id": "68Ye1KI8xKtd" + }, + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "for col in df:\n", + " print(df[col].value_counts(ascending=False), '\\n\\n\\n')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nHcHZB5vxSk9", + "outputId": "77a47af2-3b70-4ccd-d34c-5b2c3e835e76" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "3 188\n", + "2 184\n", + "1 181\n", + "4 178\n", + "Name: season, dtype: int64 \n", + "\n", + "\n", + "\n", + "1 366\n", + "0 365\n", + "Name: yr, dtype: int64 \n", + "\n", + "\n", + "\n", + "1 62\n", + "3 62\n", + "5 62\n", + "7 62\n", + "8 62\n", + "10 62\n", + "12 62\n", + "4 60\n", + "6 60\n", + "9 60\n", + "11 60\n", + "2 57\n", + "Name: mnth, dtype: int64 \n", + "\n", + "\n", + "\n", + "0 710\n", + "1 21\n", + "Name: holiday, dtype: int64 \n", + "\n", + "\n", + "\n", + "6 105\n", + "0 105\n", + "1 105\n", + "2 104\n", + "3 104\n", + "4 104\n", + "5 104\n", + "Name: weekday, dtype: int64 \n", + "\n", + "\n", + "\n", + "1 500\n", + "0 231\n", + "Name: workingday, dtype: int64 \n", + "\n", + "\n", + "\n", + "1 463\n", + "2 247\n", + "3 21\n", + "Name: weathersit, dtype: int64 \n", + "\n", + "\n", + "\n", + "0.635000 5\n", + "0.265833 5\n", + "0.680000 4\n", + "0.710833 4\n", + "0.564167 4\n", + " ..\n", + "0.669167 1\n", + "0.643333 1\n", + "0.707059 1\n", + "0.700000 1\n", + "0.215833 1\n", + "Name: temp, Length: 499, dtype: int64 \n", + "\n", + "\n", + "\n", + "0.654688 4\n", + "0.375621 3\n", + "0.637008 3\n", + "0.571975 2\n", + "0.466525 2\n", + " ..\n", + "0.578946 1\n", + "0.609229 1\n", + "0.602130 1\n", + "0.626900 1\n", + "0.223487 1\n", + "Name: atemp, Length: 690, dtype: int64 \n", + "\n", + "\n", + "\n", + "0.613333 4\n", + "0.605000 3\n", + "0.590000 3\n", + "0.538333 3\n", + "0.690000 3\n", + " ..\n", + "0.548333 1\n", + "0.561765 1\n", + "0.850000 1\n", + "0.761250 1\n", + "0.577500 1\n", + "Name: hum, Length: 595, dtype: int64 \n", + "\n", + "\n", + "\n", + "0.134954 3\n", + "0.228858 3\n", + "0.136817 3\n", + "0.110700 3\n", + "0.118792 3\n", + " ..\n", + "0.206467 1\n", + "0.212696 1\n", + "0.343943 1\n", + "0.097021 1\n", + "0.154846 1\n", + "Name: windspeed, Length: 650, dtype: int64 \n", + "\n", + "\n", + "\n", + "5409 2\n", + "2424 2\n", + "5698 2\n", + "4459 2\n", + "5119 2\n", + " ..\n", + "5046 1\n", + "4713 1\n", + "4763 1\n", + "4785 1\n", + "2729 1\n", + "Name: cnt, Length: 696, dtype: int64 \n", + "\n", + "\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "Independant_variables=df.drop(['temp'],axis=1)\n", + "Dependant_variable=df['temp']" + ], + "metadata": { + "id": "dOFiEnjIxaTs" + }, + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# train test split\n", + "X_train,X_test,y_train,y_test=train_test_split(Independant_variables,Dependant_variable,test_size=0.3)" + ], + "metadata": { + "id": "jkEFQVV4xzhw" + }, + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(X_train.shape,y_train.shape)\n", + "print(X_test.shape,y_test.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mDO7x-Alxzdx", + "outputId": "98d4004b-d3ec-4a19-d14d-19a85af98f69" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(511, 11) (511,)\n", + "(220, 11) (220,)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Fitting Linear Regression to the dataset\n", + "model=LinearRegression()\n", + "model.fit(X_train,y_train)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vhO3VaYcyAm7", + "outputId": "a2fb51a1-d627-4460-c240-d68ca85ac56b" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ] + }, + { + "cell_type": "code", + "source": [ + "model.score(X_test,y_test)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kTeRNOulyDvy", + "outputId": "b4c40c1b-523e-4285-c1cc-585f2e297174" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.9618679848468319" + ] + }, + "metadata": {}, + "execution_count": 23 + } + ] + }, + { + "cell_type": "code", + "source": [ + "y_pred = model.predict(X_test)\n", + "print(\"Predicted values:\")\n", + "print(y_pred)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yn4_5gFzyLaj", + "outputId": "01d497a6-adc0-4503-8b08-89c5bfc8d018" + }, + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Predicted values:\n", + "[0.26139833 0.24843521 0.57131762 0.46340071 0.73222751 0.42925864\n", + " 0.65221116 0.30942026 0.27721202 0.33599549 0.24746918 0.5244218\n", + " 0.72728082 0.707459 0.5515109 0.79293599 0.69154659 0.59194944\n", + " 0.53415555 0.24943123 0.42990328 0.4123266 0.1686194 0.75294334\n", + " 0.27959153 0.57164315 0.69695136 0.62093264 0.65262078 0.21879391\n", + " 0.75169612 0.78289948 0.39884028 0.25300551 0.22447105 0.6078554\n", + " 0.2495403 0.58064256 0.62074778 0.41619943 0.39082596 0.60387529\n", + " 0.54512197 0.61977368 0.38064467 0.30284106 0.28435554 0.6628038\n", + " 0.69963989 0.32409023 0.43345099 0.75005181 0.63043925 0.6819958\n", + " 0.47599378 0.69343173 0.31038888 0.25798883 0.7767981 0.31584768\n", + " 0.69432705 0.55722329 0.39954645 0.25184504 0.18177377 0.36186834\n", + " 0.5507138 0.18857743 0.75539344 0.31570068 0.52105538 0.6341357\n", + " 0.67282208 0.50166654 0.29392652 0.62884028 0.32604945 0.63985031\n", + " 0.27612366 0.53013777 0.23698819 0.33029211 0.50623254 0.56397112\n", + " 0.59818857 0.71825545 0.25223548 0.6706068 0.17602199 0.39689776\n", + " 0.64003218 0.43404124 0.67042189 0.07511919 0.4623536 0.61195081\n", + " 0.38719967 0.28576807 0.5443404 0.42866563 0.50813055 0.31119618\n", + " 0.5429557 0.36574452 0.6747021 0.17484689 0.19794907 0.24638034\n", + " 0.57279308 0.69141186 0.5743126 0.77497681 0.7871679 0.23044344\n", + " 0.26007091 0.35365621 0.3260192 0.70599096 0.68252517 0.35467262\n", + " 0.36480047 0.38318989 0.47098053 0.24829875 0.75243295 0.43966285\n", + " 0.43548234 0.43655537 0.16437059 0.3196555 0.33029807 0.49144516\n", + " 0.30761803 0.53357889 0.39495703 0.72686136 0.31485003 0.44623891\n", + " 0.73364368 0.41215329 0.71067627 0.70796604 0.65106689 0.29479719\n", + " 0.78771798 0.37393705 0.16841837 0.43583229 0.4703323 0.80768506\n", + " 0.3452518 0.38323456 0.40320437 0.5779206 0.27402613 0.29245606\n", + " 0.76606316 0.55641492 0.75096604 0.39289307 0.76567483 0.45508641\n", + " 0.25216835 0.72826098 0.47814245 0.2186926 0.54514164 0.47080859\n", + " 0.47248718 0.62200077 0.53283727 0.51041735 0.45235661 0.38388501\n", + " 0.81835626 0.45301072 0.62550536 0.09873213 0.21604778 0.25129175\n", + " 0.35903034 0.69031484 0.69331629 0.33627929 0.3912977 0.69414387\n", + " 0.44862737 0.34760098 0.5940439 0.58038327 0.64867188 0.35062691\n", + " 0.57461317 0.59909018 0.40147666 0.5974061 0.71063735 0.32438582\n", + " 0.23513132 0.79521049 0.2489857 0.44528626 0.68370916 0.26047469\n", + " 0.5500718 0.51146117 0.54201142 0.46168646 0.46735153 0.69765124\n", + " 0.3259509 0.52784103 0.7553458 0.72303439 0.41283342 0.70875842\n", + " 0.69221006 0.24677735 0.61404769 0.7626857 ]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "mean_squared_error(y_test,y_pred)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bNEN2Pf2yLWB", + "outputId": "471cdb61-c180-48ff-8fb8-a2249db7ff10" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.0012685416562742769" + ] + }, + "metadata": {}, + "execution_count": 25 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.xlabel('y_test')\n", + "plt.ylabel('y_pred')\n", + "plt.scatter(y_test,y_pred)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "id": "_iOuMT79yTic", + "outputId": "bcecedd3-5152-4d34-c8eb-548130c2fbd3" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 26 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + } + ] +} \ No newline at end of file From 78fd733b8d77980c4ee4cec7668e11d06a27c50f Mon Sep 17 00:00:00 2001 From: AtharvInamdar <90698481+AtharvInamdar@users.noreply.github.com> Date: Thu, 24 Feb 2022 22:24:12 +0530 Subject: [PATCH 2/2] Update Student assignment updates.txt --- Student assignment updates.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/Student assignment updates.txt b/Student assignment updates.txt index 9979d7c..9ad8571 100644 --- a/Student assignment updates.txt +++ b/Student assignment updates.txt @@ -1,2 +1,5 @@ Write your name and PRN no Hello Updated + +Name-Inamdar Atharv Nitin +PRN-2019BTECS00051