diff --git a/Dataset.ipynb b/Dataset.ipynb new file mode 100644 index 0000000..84a47bc --- /dev/null +++ b/Dataset.ipynb @@ -0,0 +1,619 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Untitled0.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.linear_model import LinearRegression\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split" + ], + "metadata": { + "id": "vXod3zGa-y6d" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df = pd.read_csv(\"/content/Nutrition__Physical_Activity__and_Obesity_-_Behavioral_Risk_Factor_Surveillance_System.csv\")" + ], + "metadata": { + "id": "Hnd3nFIo_mxz" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df.describe()" + ], + "metadata": { + "id": "wv-UVxpC_see", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 364 + }, + "outputId": "c16fa6aa-581d-4132-b1ae-e2a12f16ce75" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
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YearStartYearEndData_Value_UnitData_ValueData_Value_AltLow_Confidence_LimitHigh_Confidence_LimitSample_SizeLocationID
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mean2013.2814652013.281465NaN31.15668131.15668126.89222735.9899973889.1928630.282215
std1.6933001.693300NaN10.24703310.24703310.03858411.20581319829.4212916.821318
min2011.0000002011.000000NaN0.9000000.9000000.3000003.00000050.000001.000000
25%2012.0000002012.000000NaN24.10000024.10000020.00000028.200000566.0000017.000000
50%2013.0000002013.000000NaN30.70000030.70000026.45000035.6000001209.0000030.000000
75%2015.0000002015.000000NaN37.00000037.00000032.90000042.2000002519.0000044.000000
max2016.0000002016.000000NaN77.60000077.60000069.50000087.700000476876.0000078.000000
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Sample_Size LocationID\n", + "count 53392.000000 53392.000000 ... 48346.00000 53392.000000\n", + "mean 2013.281465 2013.281465 ... 3889.19286 30.282215\n", + "std 1.693300 1.693300 ... 19829.42129 16.821318\n", + "min 2011.000000 2011.000000 ... 50.00000 1.000000\n", + "25% 2012.000000 2012.000000 ... 566.00000 17.000000\n", + "50% 2013.000000 2013.000000 ... 1209.00000 30.000000\n", + "75% 2015.000000 2015.000000 ... 2519.00000 44.000000\n", + "max 2016.000000 2016.000000 ... 476876.00000 78.000000\n", + "\n", + "[8 rows x 9 columns]" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ] + }, + { + "cell_type": "code", + "source": [ + "wrangled = df[df['StratificationID1'] == 'OVERALL'][['LocationDesc','Data_Value', 'Question', \"YearStart\" ]]\n", + "question = wrangled[wrangled['Question'] == 'Percent of adults who engage in no leisure-time physical activity'][['LocationDesc','Data_Value', 'Question', \"YearStart\" ]]\n", + "x_all = question[question['YearStart'] == 2014][['LocationDesc','Data_Value' ]]\n", + "x = question[question['YearStart'] == 2014][['Data_Value' ]].values" + ], + "metadata": { + "id": "zKQx268TAGJJ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "x = x.reshape(-1, 1)\n", + "x" + ], + "metadata": { + "id": "-EkjcqgwALeM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "04075c7f-b9a5-4e6e-af71-e0989fd0e395" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[27.6],\n", + " [19.2],\n", + " [21.2],\n", + " [30.7],\n", + " [21.7],\n", + " [16.4],\n", + " [20.6],\n", + " [24.9],\n", + " [20.8],\n", + " [23.7],\n", + " [23.6],\n", + " [27.7],\n", + " [19.6],\n", + " [18.7],\n", + " [23.9],\n", + " [26.1],\n", + " [22.6],\n", + " [23.8],\n", + " [28.2],\n", + " [29.5],\n", + " [19.7],\n", + " [21.4],\n", + " [20.1],\n", + " [25.5],\n", + " [20.2],\n", + " [31.6],\n", + " [25. ],\n", + " [19.6],\n", + " [23.7],\n", + " [21.3],\n", + " [22.5],\n", + " [19.3],\n", + " [23.3],\n", + " [23.3],\n", + " [25.9],\n", + " [23.2],\n", + " [21.3],\n", + " [25. ],\n", + " [28.3],\n", + " [16.5],\n", + " [23.3],\n", + " [40.6],\n", + " [22.5],\n", + " [25.3],\n", + " [21.2],\n", + " [26.8],\n", + " [27.6],\n", + " [16.8],\n", + " [19. ],\n", + " [23.5],\n", + " [18.1],\n", + " [28.7],\n", + " [21.2],\n", + " [22.1]])" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "source": [ + "wrangled = df[df['StratificationID1'] == 'OVERALL'][['LocationDesc','Data_Value', 'Question', \"YearStart\" ]]\n", + "year = wrangled[wrangled['Question'] == 'Percent of adults aged 18 years and older who have obesity'][['LocationDesc','Data_Value', 'Question', \"YearStart\" ]]\n", + "y_all = year[year['YearStart'] == 2014][['LocationDesc','Data_Value' ]]\n", + "y = year[year['YearStart'] == 2014][['Data_Value' ]].values" + ], + "metadata": { + "id": "PiJYgmujAO-Y" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "y = y.reshape(-1, 1)\n", + "y" + ], + "metadata": { + "id": "2WmeBhj0AU0G", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1a7594de-20d9-4bcf-e20c-cf8d1d512ba1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[33.5],\n", + " [29.7],\n", + " [28.9],\n", + " [35.9],\n", + " [24.7],\n", + " [21.3],\n", + " [26.3],\n", + " [30.7],\n", + " [21.7],\n", + " [26.2],\n", + " [30.5],\n", + " [28. ],\n", + " [22.1],\n", + " [28.9],\n", + " [29.3],\n", + " [32.7],\n", + " [30.9],\n", + " [31.3],\n", + " [31.6],\n", + " [34.9],\n", + " [28.2],\n", + " [29.6],\n", + " [23.3],\n", + " [30.7],\n", + " [27.6],\n", + " [35.5],\n", + " [30.2],\n", + " [26.4],\n", + " [28.9],\n", + " [30.2],\n", + " [27.7],\n", + " [27.4],\n", + " [26.9],\n", + " [28.4],\n", + " [27. ],\n", + " [29.7],\n", + " [32.2],\n", + " [32.6],\n", + " [33. ],\n", + " [27.9],\n", + " [30.2],\n", + " [28.3],\n", + " [27. ],\n", + " [32.1],\n", + " [29.8],\n", + " [31.2],\n", + " [31.9],\n", + " [25.7],\n", + " [24.8],\n", + " [28.5],\n", + " [27.3],\n", + " [35.7],\n", + " [31.2],\n", + " [29.5]])" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(x, y,train_size=.8, test_size=.2, random_state=100)" + ], + "metadata": { + "id": "5XHItcGvAY8B" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(x_train, y_train, color='orange')\n", + "plt.xlabel('% Adults with reporting no leisure Physical Activity')\n", + "plt.ylabel('% of Adults who have Obesity')\n", + "plt.title('Physical Data')\n", + "plt.show()" + ], + "metadata": { + "id": "JnBVBxH7Abop", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + }, + "outputId": "d6b32e6b-c1f8-4774-c4e1-fa7d62d6b4a4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "lm = LinearRegression()\n", + "lm.fit(x_train, y_train)\n", + "y_predict = lm.predict(x_test)" + ], + "metadata": { + "id": "fOuV2No0AmF2" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(f'Train Accuracy {round(lm.score(x_train, y_train)* 100,2)}%')\n", + "print(f'Test Accuracy {round(lm.score(x_test, y_test)* 100,2)}%')" + ], + "metadata": { + "id": "3SamOFQXArBJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1db20609-1bc5-4c97-ec0f-1810e15404e9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train Accuracy 29.76%\n", + "Test Accuracy 44.09%\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(x_train, y_train, color='orange')\n", + "plt.scatter(x_test, y_predict, color='red')\n", + "plt.show()" + ], + "metadata": { + "id": "2SGD1B8J-qaW", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "outputId": "5140fabe-723b-4e7e-a4df-543c4ebdb1c7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "print('Intercept', lm.intercept_)\n", + "print('Coefficient', lm.coef_)" + ], + "metadata": { + "id": "EpWqu6YzA8ou", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "440cc0e7-58d8-4af8-870f-5a78ec761abf" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Intercept [19.71194003]\n", + "Coefficient [[0.41271641]]\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/Student assignment updates.txt b/Student assignment updates.txt index 9979d7c..c628966 100644 --- a/Student assignment updates.txt +++ b/Student assignment updates.txt @@ -1,2 +1,5 @@ Write your name and PRN no Hello Updated + +Atharav Patil +2019BTECS00112