simplelinearmodel-lifesatisfaction-gdppercapita.ipynb

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    "tags": []
   },
   "source": [
    "**I am writing a simple Linear Model to predict the Life Satisfaction value based on the GDP per capita (USD) provided for the given country.\n",
    "This is my practice model which I learned from the book 'Hands-on Machine Learning with Scikit-Learn, Keras, and Tensorflow' by Aurelien Geron**"
   ]
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   },
   "source": [
    "**Importing necessary libraries**"
   ]
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    "execution": {
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   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2239629f",
   "metadata": {
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     "start_time": "2023-02-06T15:06:46.319722",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Obtaining data from the Author's github repository and preparing the data**"
   ]
  },
  {
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   "execution_count": 2,
   "id": "400fb76e",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "data_root=\"https://github.com/ageron/data/raw/main/\"\n",
    "lifesat=pd.read_csv(data_root+\"lifesat/lifesat.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4824ba8f",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "**Based on the data obtained from the github repository, a scatter plot is prepared to view the data better. Here we can see the correlation between the Life satisfaction and GDP per capita (USD) in a better way. **"
   ]
  },
  {
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   "execution_count": 3,
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   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X=lifesat[[\"GDP per capita (USD)\"]].values\n",
    "y=lifesat[[\"Life satisfaction\"]].values\n",
    "lifesat.plot(kind='scatter',grid=True,x=\"GDP per capita (USD)\",y=\"Life satisfaction\")\n",
    "plt.axis([23_500,62_500,4,9])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b751c51f",
   "metadata": {
    "papermill": {
     "duration": 0.002757,
     "end_time": "2023-02-06T15:06:47.196412",
     "exception": false,
     "start_time": "2023-02-06T15:06:47.193655",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Now I am training a model in linear regression. I am trying to predict Life satisfaction value for the country Cyprus whose GDP per capita (USD) is 37655.2.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "03df3dff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-06T15:06:47.204272Z",
     "iopub.status.busy": "2023-02-06T15:06:47.203791Z",
     "iopub.status.idle": "2023-02-06T15:06:47.228617Z",
     "shell.execute_reply": "2023-02-06T15:06:47.227383Z"
    },
    "papermill": {
     "duration": 0.031857,
     "end_time": "2023-02-06T15:06:47.231217",
     "exception": false,
     "start_time": "2023-02-06T15:06:47.199360",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.30165767]]\n"
     ]
    }
   ],
   "source": [
    "model=LinearRegression()\n",
    "model.fit(X,y)\n",
    "\n",
    "X_new=[[37_655.2]]\n",
    "print(model.predict(X_new))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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