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1,273 changes: 1,273 additions & 0 deletions Riya_Agrahari.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "MWuMU9_NxGPR"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import sklearn"
]
},
{
"cell_type": "code",
"source": [
"from sklearn.datasets import load_boston\n",
"df = load_boston()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "T2M8Ialmx7cN",
"outputId": "1a56c068-21ef-4335-f540-cea63954e856"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
"\n",
" The Boston housing prices dataset has an ethical problem. You can refer to\n",
" the documentation of this function for further details.\n",
"\n",
" The scikit-learn maintainers therefore strongly discourage the use of this\n",
" dataset unless the purpose of the code is to study and educate about\n",
" ethical issues in data science and machine learning.\n",
"\n",
" In this special case, you can fetch the dataset from the original\n",
" source::\n",
"\n",
" import pandas as pd\n",
" import numpy as np\n",
"\n",
"\n",
" data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
" raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
" data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
" target = raw_df.values[1::2, 2]\n",
"\n",
" Alternative datasets include the California housing dataset (i.e.\n",
" :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
" dataset. You can load the datasets as follows::\n",
"\n",
" from sklearn.datasets import fetch_california_housing\n",
" housing = fetch_california_housing()\n",
"\n",
" for the California housing dataset and::\n",
"\n",
" from sklearn.datasets import fetch_openml\n",
" housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
"\n",
" for the Ames housing dataset.\n",
" \n",
" warnings.warn(msg, category=FutureWarning)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"df.keys()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4Wz5kUKjypxx",
"outputId": "1d41e145-fd55-4cc2-dba9-bcc6c8f84413"
},
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename', 'data_module'])"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"source": [
"print(df.DESCR)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZtK71kP6zpz7",
"outputId": "9d1f63b5-c20e-4ea8-8bb3-4021e0a5359d"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
".. _boston_dataset:\n",
"\n",
"Boston house prices dataset\n",
"---------------------------\n",
"\n",
"**Data Set Characteristics:** \n",
"\n",
" :Number of Instances: 506 \n",
"\n",
" :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
"\n",
" :Attribute Information (in order):\n",
" - CRIM per capita crime rate by town\n",
" - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n",
" - INDUS proportion of non-retail business acres per town\n",
" - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
" - NOX nitric oxides concentration (parts per 10 million)\n",
" - RM average number of rooms per dwelling\n",
" - AGE proportion of owner-occupied units built prior to 1940\n",
" - DIS weighted distances to five Boston employment centres\n",
" - RAD index of accessibility to radial highways\n",
" - TAX full-value property-tax rate per $10,000\n",
" - PTRATIO pupil-teacher ratio by town\n",
" - B 1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\n",
" - LSTAT % lower status of the population\n",
" - MEDV Median value of owner-occupied homes in $1000's\n",
"\n",
" :Missing Attribute Values: None\n",
"\n",
" :Creator: Harrison, D. and Rubinfeld, D.L.\n",
"\n",
"This is a copy of UCI ML housing dataset.\n",
"https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
"\n",
"\n",
"This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
"\n",
"The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
"prices and the demand for clean air', J. Environ. Economics & Management,\n",
"vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
"...', Wiley, 1980. N.B. Various transformations are used in the table on\n",
"pages 244-261 of the latter.\n",
"\n",
"The Boston house-price data has been used in many machine learning papers that address regression\n",
"problems. \n",
" \n",
".. topic:: References\n",
"\n",
" - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
" - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(df.filename)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UxLYtQmEz6iy",
"outputId": "a6b6f3ce-ad31-4468-f6b4-7ca812004a3d"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"boston_house_prices.csv\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(df.target)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EPQ5yW9m0NP6",
"outputId": "e8ebe20b-b097-4474-d516-f129f57a2322"
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{
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"name": "stdout",
"text": [
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" 18.4 21. 12.7 14.5 13.2 13.1 13.5 18.9 20. 21. 24.7 30.8 34.9 26.6\n",
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" 24.7 31.6 23.3 19.6 18.7 16. 22.2 25. 33. 23.5 19.4 22. 17.4 20.9\n",
" 24.2 21.7 22.8 23.4 24.1 21.4 20. 20.8 21.2 20.3 28. 23.9 24.8 22.9\n",
" 23.9 26.6 22.5 22.2 23.6 28.7 22.6 22. 22.9 25. 20.6 28.4 21.4 38.7\n",
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" 17. 15.6 13.1 41.3 24.3 23.3 27. 50. 50. 50. 22.7 25. 50. 23.8\n",
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" 37.9 32.5 26.4 29.6 50. 32. 29.8 34.9 37. 30.5 36.4 31.1 29.1 50.\n",
" 33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5 50. 22.6 24.4 22.5 24.4 20.\n",
" 21.7 19.3 22.4 28.1 23.7 25. 23.3 28.7 21.5 23. 26.7 21.7 27.5 30.1\n",
" 44.8 50. 37.6 31.6 46.7 31.5 24.3 31.7 41.7 48.3 29. 24. 25.1 31.5\n",
" 23.7 23.3 22. 20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8\n",
" 29.6 42.8 21.9 20.9 44. 50. 36. 30.1 33.8 43.1 48.8 31. 36.5 22.8\n",
" 30.7 50. 43.5 20.7 21.1 25.2 24.4 35.2 32.4 32. 33.2 33.1 29.1 35.1\n",
" 45.4 35.4 46. 50. 32.2 22. 20.1 23.2 22.3 24.8 28.5 37.3 27.9 23.9\n",
" 21.7 28.6 27.1 20.3 22.5 29. 24.8 22. 26.4 33.1 36.1 28.4 33.4 28.2\n",
" 22.8 20.3 16.1 22.1 19.4 21.6 23.8 16.2 17.8 19.8 23.1 21. 23.8 23.1\n",
" 20.4 18.5 25. 24.6 23. 22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1\n",
" 19.5 18.5 20.6 19. 18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1 24.5 26.6\n",
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" 21.9 27.5 21.9 23.1 50. 50. 50. 50. 50. 13.8 13.8 15. 13.9 13.3\n",
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" 11.9 27.9 17.2 27.5 15. 17.2 17.9 16.3 7. 7.2 7.5 10.4 8.8 8.4\n",
" 16.7 14.2 20.8 13.4 11.7 8.3 10.2 10.9 11. 9.5 14.5 14.1 16.1 14.3\n",
" 11.7 13.4 9.6 8.7 8.4 12.8 10.5 17.1 18.4 15.4 10.8 11.8 14.9 12.6\n",
" 14.1 13. 13.4 15.2 16.1 17.8 14.9 14.1 12.7 13.5 14.9 20. 16.4 17.7\n",
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" 16.7 12. 14.6 21.4 23. 23.7 25. 21.8 20.6 21.2 19.1 20.6 15.2 7.\n",
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"boston= pd.DataFrame(df.data, columns = df.feature_names)\n",
"boston.head()"
],
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"\n",
" PTRATIO B LSTAT \n",
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"1 17.8 396.90 9.14 \n",
"2 17.8 392.83 4.03 \n",
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{
"cell_type": "code",
"source": [
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"boston.head()"
],
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"height": 270
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"id": "fs9Y3jHf1OoE",
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"outputs": [
{
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"data": {
"text/plain": [
" CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\n",
"0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 \n",
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"RAD 0\n",
"TAX 0\n",
"PTRATIO 0\n",
"B 0\n",
"LSTAT 0\n",
"MEDV 0\n",
"dtype: int64"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.model_selection import train_test_split\n",
"X = boston.drop('MEDV', axis=1)\n",
"Y = boston['MEDV']\n",
"X_train,X_test,Y_train,Y_test= train_test_split(X,Y,test_size =0.15, random_state=5)\n",
"print(X_train.shape)\n",
"print(X_test.shape)\n",
"print(Y_train.shape)\n",
"print(Y_test.shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fvRAOtIt21Oq",
"outputId": "e4c53baa-77e0-436a-dcd5-f944e36e0018"
},
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(430, 13)\n",
"(76, 13)\n",
"(430,)\n",
"(76,)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error"
],
"metadata": {
"id": "5gUqttGU5Bhe"
},
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"source": [
"lin_model = LinearRegression()\n",
"lin_model.fit(X_train, Y_train)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "U1Tsfl995ypK",
"outputId": "7a44db4c-4408-4d2c-ada6-0af156ec78dc"
},
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LinearRegression()"
]
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"source": [
"y_train_predict = lin_model.predict(X_train)\n",
"rmse = (np.sqrt(mean_squared_error(Y_train, y_train_predict)))\n",
"\n",
"print(\"The model performance for training set\")\n",
"print('RMSE IS {}'.format(rmse))\n",
"print(\"\\n\")\n",
"\n",
"y_test_predict = lin_model.predict(X_test)\n",
"rmse = (np.sqrt(mean_squared_error(Y_test,y_test_predict)))\n",
"\n",
"print(\"The model performance for testing set\")\n",
"print('RMSE is {}'.format(rmse))\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "A9KNyTeS6dOG",
"outputId": "54828583-4676-4aed-aa55-68d84fa28dd7"
},
"execution_count": 27,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The model performance for training set\n",
"RMSE IS 4.710901797319796\n",
"\n",
"\n",
"The model performance for testing set\n",
"RMSE is 4.687543527902972\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "uHz8EHFK8sEf"
},
"execution_count": null,
"outputs": []
}
]
}