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Linear Regressin Model Example 01.ipynb
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Book.csv
videos views
channel1 30 34000
channel2 40 41000
channel3 50 42500
channel4 60 54300
channel5 70 56000
Display the source blob
Display the rendered blob
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linear-regressin-model-example-01.ipynb
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"<a href=\"https://colab.research.google.com/gist/Deshan555/c44b06a7e15649f434a17fad3fc3adac/linear-regressin-model-example-01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (1.21.6)\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.2.2)\n",
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"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.4.3)\n",
"Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.21.6)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.2)\n",
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"Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.3.5)\n",
"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2022.1)\n",
"Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (1.21.6)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.2)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: sklearn in /usr/local/lib/python3.7/dist-packages (0.0)\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sklearn) (1.0.2)\n",
"Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn) (1.21.6)\n",
"Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn) (1.4.1)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn) (1.1.0)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn) (3.1.0)\n"
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"source": [
"# install python modules for the project\n",
"\n",
"% pip install numpy\n",
"\n",
"% pip install matplotlib\n",
"\n",
"% pip install pandas\n",
"\n",
"% pip install sklearn"
]
},
{
"cell_type": "code",
"source": [
"# import python important models\n",
"\n",
"import numpy as np\n",
"\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.linear_model import LinearRegression"
],
"metadata": {
"id": "30sI2BlOklcY"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"data = pd.read_csv('Book.csv')\n",
"\n",
"data"
],
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "Ltcvek4tkqkP",
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"data": {
"text/plain": [
" Unnamed: 0 videos views\n",
"0 channel1 30 34000\n",
"1 channel2 40 41000\n",
"2 channel3 50 42500\n",
"3 channel4 60 54300\n",
"4 channel5 70 56000"
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>videos</th>\n",
" <th>views</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" <td>60</td>\n",
" <td>54300</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>channel5</td>\n",
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},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [
"plt.scatter(data.videos, data.views, color='blue')\n",
"\n",
"plt.xlabel('Number Of Videos')\n",
"\n",
"plt.ylabel('Number Of Views')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
},
"id": "d_eXvUzpk1Ax",
"outputId": "1daac0e9-98f5-4680-eae6-f0e186880914"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0, 0.5, 'Number Of Views')"
]
},
"metadata": {},
"execution_count": 8
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"data.videos"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Lkr0DdTXmiPF",
"outputId": "0ce334aa-1994-47f1-bbe2-443225bec003"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 30\n",
"1 40\n",
"2 50\n",
"3 60\n",
"4 70\n",
"Name: videos, dtype: int64"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"data.views"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YkALe7dpnO7q",
"outputId": "ca8fe288-880f-45c6-da5b-ce37cab75e46"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 34000\n",
"1 41000\n",
"2 42500\n",
"3 54300\n",
"4 56000\n",
"Name: views, dtype: int64"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"# convert pandas series of data into Numpy array\n",
"\n",
"video_array = np.array(data.videos.values)\n",
"\n",
"video_array\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d8zqSd9bnQwb",
"outputId": "46e92370-0d6c-45b7-e6e9-1be17cf05183"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([30, 40, 50, 60, 70])"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"source": [
"views_array = np.array(data.views.values)\n",
"\n",
"views_array"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gXdI5WXwnnZ1",
"outputId": "c17e60f4-e147-4e97-cd19-62963416d36c"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([34000, 41000, 42500, 54300, 56000])"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"model = LinearRegression()\n",
"\n",
"# we want to convert one diamentional array to 2D array\n",
"\n",
"model.fit(video_array.reshape(-1, 1), views_array)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9in_MRptn4ik",
"outputId": "83db790d-7e37-4d76-9d98-71ddac798969"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LinearRegression()"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"source": [
"# Let's predict the values using that pre trained model\n",
"\n",
"new_veriable = np.array([45]).reshape((-1, 1))\n",
"\n",
"new_veriable"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tYKO5TUtpEGl",
"outputId": "1b4f240e-704f-488d-9f0c-1a492676b347"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[45]])"
]
},
"metadata": {},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"source": [
"pred = model.predict(new_veriable)\n",
"\n",
"print(pred)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mNZgje-truQR",
"outputId": "4d165072-1aa8-47d5-d866-f5796ba22e4c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[42695.]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# draw plot using (y = m*x + c)\n",
"\n",
"plt.scatter(data.videos, data.views, color = 'red')\n",
"\n",
"m,c = np.polyfit(video_array, views_array, 1)\n",
"\n",
"plt.plot(video_array,m*video_array+c)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "ZNJ791Visf7V",
"outputId": "bcb0e292-83e1-4ef6-aeb8-c6d595f5aa04"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7ff97a804650>]"
]
},
"metadata": {},
"execution_count": 20
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"m"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nkm_m6vCwYP7",
"outputId": "8200d5bd-5964-4cc4-a432-1a7f57a30f1a"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"573.0"
]
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"source": [
"c"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dDBTOfGLwhcR",
"outputId": "62777930-0628-4599-ee69-b01c64209d7f"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"16910.000000000007"
]
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"source": [
"# Let's verify y = mx+c\n",
"\n",
"x = 45\n",
"\n",
"y = m*x+c\n",
"\n",
"print(y)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XSlc4jB0wibd",
"outputId": "7879ed6d-eae0-441d-cbc1-1d2d4f9ab57e"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"42695.00000000001\n"
]
}
]
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "ml9133LYxkwT"
},
"execution_count": null,
"outputs": []
}
]
}
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