diff --git a/armstrong no. b/armstrong no similarity index 100% rename from armstrong no. rename to armstrong no diff --git a/machine learning basics/Air QualityPrediction using Linear Regression/.ipynb_checkpoints/Air_Quality_Prediction-checkpoint.ipynb b/machine learning basics/Air QualityPrediction using Linear Regression/.ipynb_checkpoints/Air_Quality_Prediction-checkpoint.ipynb new file mode 100644 index 0000000..7a15c5b --- /dev/null +++ b/machine learning basics/Air QualityPrediction using Linear Regression/.ipynb_checkpoints/Air_Quality_Prediction-checkpoint.ipynb @@ -0,0 +1,745 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PROBLEM STATEMENT :" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Given a dataset containing air samples of different locations having 5 features of the air, predict the air quality index and compare it with the \"target\" column in the given \"Test\" file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solution:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing Libraries and Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.linear_model import LinearRegression" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "trainData = pd.read_csv('Train.csv')\n", + "testData = pd.read_csv('Test.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualising Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Preparing Data in training format - Converting dataframe to numpy arrays" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1600, 5) (1600,)\n" + ] + } + ], + "source": [ + "xTrain = dfXTrain.values \n", + "yTrain = dfYTrain.values\n", + "print(xTrain.shape,yTrain.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.29341624, -0.94559871, -0.42110515, 0.40681602, 0.52566183])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xTrain[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(400, 5) (400,)\n" + ] + } + ], + "source": [ + "xTest = dfXTest.values\n", + "yTest = dfYTest.values\n", + "print(xTest.shape,yTest.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.01525387, 2.07620944, -0.26643482, -2.4180882 , -0.98050279])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xTest[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "model = LinearRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n", + " normalize=False)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(xTrain,yTrain)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predicting" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "output = model.predict(xTest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Visualising the features LEARNED by the model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.990966735574957\n", + "[29.68187118 92.66247759 8.28062089 44.76773522 2.51916121]\n" + ] + } + ], + "source": [ + "bias = model.intercept_\n", + "coeff = model.coef_\n", + "print(bias)\n", + "print(coeff)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comparing the Predicted and True values" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.999926185789197" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.score(xTest,yTest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hence, our model predicts the Air Quality with an accuracy of 99%" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/machine learning basics/Air QualityPrediction using Linear Regression/Air_Quality_Prediction.ipynb b/machine learning basics/Air QualityPrediction using Linear Regression/Air_Quality_Prediction.ipynb new file mode 100644 index 0000000..7a15c5b --- /dev/null +++ b/machine learning basics/Air QualityPrediction using Linear Regression/Air_Quality_Prediction.ipynb @@ -0,0 +1,745 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PROBLEM STATEMENT :" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Given a dataset containing air samples of different locations having 5 features of the air, predict the air quality index and compare it with the \"target\" column in the given \"Test\" file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solution:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing Libraries and Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.linear_model import LinearRegression" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "trainData = pd.read_csv('Train.csv')\n", + "testData = pd.read_csv('Test.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualising Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " feature_1 feature_2 feature_3 feature_4 feature_5\n", + "0 0.293416 -0.945599 -0.421105 0.406816 0.525662\n", + "1 -0.836084 -0.189228 -0.776403 -1.053831 0.597997\n", + "2 0.236425 0.132836 -0.147723 0.699854 -0.187364\n", + "3 0.175312 0.143194 -0.581111 -0.122107 -1.292168\n", + "4 -1.693011 0.542712 -2.798729 -0.686723 1.244077" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfXTrain = trainData.iloc[:,:5] #exclusive of end values\n", + "dfXTrain.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 -82.154667\n", + "1 -48.897960\n", + "2 77.270371\n", + "3 -2.988581\n", + "4 -37.596722\n", + "Name: target, dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfYTrain = trainData.iloc[:,5]\n", + "dfYTrain.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Preparing Data in training format - Converting dataframe to numpy arrays" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1600, 5) (1600,)\n" + ] + } + ], + "source": [ + "xTrain = dfXTrain.values \n", + "yTrain = dfYTrain.values\n", + "print(xTrain.shape,yTrain.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.29341624, -0.94559871, -0.42110515, 0.40681602, 0.52566183])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xTrain[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(400, 5) (400,)\n" + ] + } + ], + "source": [ + "xTest = dfXTest.values\n", + "yTest = dfYTest.values\n", + "print(xTest.shape,yTest.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.01525387, 2.07620944, -0.26643482, -2.4180882 , -0.98050279])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xTest[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "model = LinearRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n", + " normalize=False)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(xTrain,yTrain)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predicting" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "output = model.predict(xTest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Visualising the features LEARNED by the model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.990966735574957\n", + "[29.68187118 92.66247759 8.28062089 44.76773522 2.51916121]\n" + ] + } + ], + "source": [ + "bias = model.intercept_\n", + "coeff = model.coef_\n", + "print(bias)\n", + "print(coeff)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comparing the Predicted and True values" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.999926185789197" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.score(xTest,yTest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hence, our model predicts the Air Quality with an accuracy of 99%" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/machine learning basics/Air QualityPrediction using Linear Regression/Test.csv b/machine learning basics/Air QualityPrediction using Linear Regression/Test.csv new 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