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ML-For-Beginners/2-Regression/1-Tools/notebook.ipynb

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{
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.5"
},
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"kernelspec": {
"name": "python3",
"display_name": "Python 3.9.5 64-bit"
},
"interpreter": {
"hash": "ac59ebe37160ed0dfa835113d9b8498d9f09ceb179beaac4002f036b9467c963"
}
},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"source": [
"# Welcome to your notebook"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 238,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"hello notebook\n"
]
}
],
"source": [
"print('hello notebook')"
]
},
{
"cell_type": "code",
"execution_count": 239,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt #for line plot\n",
"import numpy as np #handling numeric data \n",
"from sklearn import datasets, linear_model, model_selection\n"
]
},
{
"cell_type": "code",
"execution_count": 240,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(442, 10)\n[ 0.03807591 0.05068012 0.06169621 0.02187235 -0.0442235 -0.03482076\n -0.04340085 -0.00259226 0.01990842 -0.01764613]\n"
]
}
],
"source": [
"# Load the diabetes dataset\n",
"X, y = datasets.load_diabetes(return_X_y=True)\n",
"print(X.shape)\n",
"print(X[0])"
]
},
{
"source": [
"Target: Column 11 is a quantitative measure of disease progression one year after baseline\n",
"\n",
"Given this target, the data set is demonstrating disease progression for the each patient with some specific features."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 241,
"metadata": {},
"outputs": [],
"source": [
"# print(X.shape) -> 442*10 feature\n",
"X = X[:, np.newaxis, 9]\n",
"# print(X.shape) -> 442*1 feature\n"
]
},
{
"source": [
"Split the data into a test set and a training set for the axis and the colmns "
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 242,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n",
"# print(X_test.shape)\n",
"# print(y_test.shape)\n",
"# print(y_train.shape)\n",
"# print(X_train.shape)"
]
},
{
"source": [
"Creation of a Linear model (using linear regression) from the training subsets"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 243,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LinearRegression()"
]
},
"metadata": {},
"execution_count": 243
}
],
"source": [
" model = linear_model.LinearRegression()\n",
" model.fit(X_train, y_train)"
]
},
{
"source": [
"Making predictions"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 244,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_test)"
]
},
{
"source": [
"Making a graphic represenntation with the actual testing data and the linear estimations(can be represented by a line)."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 245,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
" plt.scatter(X_test, y_test, color='black')\n",
" plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
" plt.show()"
]
},
{
"source": [
"## assignement"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 246,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[ 5. 162. 60.]\n [ 2. 110. 60.]\n [ 12. 101. 101.]\n [ 12. 105. 37.]\n [ 13. 155. 58.]\n [ 4. 101. 42.]\n [ 8. 101. 38.]\n [ 6. 125. 40.]\n [ 15. 200. 40.]\n [ 17. 251. 250.]\n [ 17. 120. 38.]\n [ 13. 210. 115.]\n [ 14. 215. 105.]\n [ 1. 50. 50.]\n [ 6. 70. 31.]\n [ 12. 210. 120.]\n [ 4. 60. 25.]\n [ 11. 230. 80.]\n [ 15. 225. 73.]\n [ 2. 110. 43.]]\n"
]
}
],
"source": [
"X ,y= datasets.load_linnerud( return_X_y=True, as_frame=False)\n",
"# print(X.shape)\n",
"# print(X[0])\n",
"# Target \n",
"print(X)\n",
"X = X[:, np.newaxis, 1]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
]
}
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