LinearRegression_Project-Gia-Nathan-Final.Version.07.11.2023.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9MMoEomXh5BU"
   },
   "source": [
    "**Copyright: © NexStream Technical Education, LLC**.  \n",
    "All rights reserved"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uucNID33jORm"
   },
   "source": [
    "#**PART 1**:  Simple Linear Regression - Manual Calculation\n",
    "In this project, you will perform a manual calculation of the coefficients used in a simple linear regression model for a given dataset.\n",
    "Please reference the video lectures on Simple and Multiple Linear Regression for a description of this example and the main functions you will need in the implementation.\n",
    "Please complete the following steps in your Colab Script.  The reference script below provides template code and hints to help with each step.  You will be turning in code and screenshots of your console output in an accompanying assignment.\n",
    "\n",
    "-  **Step 1**:  For the input parameters (dataset) given in the cell below, create a scatter plot of Glucose level (y parameter) vs. Age (x parameter).\n",
    "-  **Step 2**:  Calculate the coefficients for a simple linear regression model using the following equations:  (NOTE, you may NOT use any machine learning library models for this step - you must calculate the parameters use the equations shown).  \n",
    "\n",
    "$$\\hat y = b_0+b_1x_1$$  \n",
    "$$b_1=\\frac {\\sum\n",
    "(x_n-\\bar x)(y_n-\\bar y)} {\\sum\n",
    "(x_n-\\bar x)^2}\n",
    "$$  \n",
    "$$b_0=\\bar y-b_1\\bar x$$  \n",
    "\n",
    "-  **Step 3**:  Create a scatter plot of the input dataset and generated regression line on the same plot. \n",
    "-  **Step 4**:  Calculate the performance using R-squared using sklearn r2_score function (you may use the sklearn library for this step).\n",
    "-  **Step 5**:  Confirm your calculated coefficients and R-squared performance metric using the embedded doctest module.  Record your regression equation - you will need to upload this to the accompanying assignment.\n",
    "-  **Step 6**:  Reflect on the performance of your model.  What does the R-squared parameter tell you about the properties of this dataset and your simple linear prediction?  For example, is age a good predictor of glucose level?  You will answer this question in the accompanying assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Qo_-aDdr01sb"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0: 67.348\n",
      "b1: 0.354\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.357\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TestResults(failed=0, attempted=3)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Simple Linear Regression - Manual Calculation\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import r2_score\n",
    "%matplotlib inline\n",
    "\n",
    "#Input Parameters\n",
    "Age = np.array([43,21,25,42,57,59,35,15,55,50,65,10,45,35])\n",
    "Glucose = np.array([99,65,79,75,87,81,80,80,90,70,95,67,90,82])\n",
    "\n",
    "\n",
    "#Step 1:  Create a scatter plot of Glucose level (y parameter) vs. Age (x parameter). \n",
    "#         Your plot must include x and y labels.\n",
    "# YOUR CODE HERE...\n",
    "\n",
    "plt.scatter(Age, Glucose)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Glucose Level')\n",
    "plt.title('Scatter Plot of Glucose Level vs. Age')\n",
    "plt.show()\n",
    "\n",
    "#Step 2:  Create a function which MANALLY (using the equations) calculates the \n",
    "#         coefficients for a simple linear regression model.\n",
    "#         Your function must input numpy arrays for the x and y variables and return b0 and b1.\n",
    "#         Your function MUST use equations shown in the text cell above. \n",
    "#         (NOTE - you may not use a model API for this step, calculate the parameters use the equations above).\n",
    "def simpleLRcoeffsManual(X, Y):\n",
    "# return the tuple b0, b1 in this order\n",
    "# YOUR CODE HERE...\n",
    "\n",
    "    x_mean = np.mean(X)\n",
    "    y_mean = np.mean(Y)\n",
    "    numerator = np.sum((X - x_mean) * (Y - y_mean))\n",
    "    denominator = np.sum((X - x_mean) ** 2)\n",
    "    b1 = numerator / denominator\n",
    "    b0 = y_mean - b1 * x_mean\n",
    "    return b0, b1\n",
    "\n",
    "\n",
    "\n",
    "b0, b1 = simpleLRcoeffsManual(Age, Glucose)\n",
    "print('b0:', round(b0, 3))\n",
    "print('b1:', round(b1, 3))\n",
    "\n",
    "\n",
    "#Step 3:  Create a scatter plot of the input dataset and generated regression line (yhat = x*b1 + b0) on the same plot.\n",
    "# YOUR CODE HERE...\n",
    "\n",
    "yhat = b0 + b1 * Age\n",
    "\n",
    "plt.scatter(Age, Glucose)\n",
    "plt.plot(Age, yhat, color='red')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Glucose Level')\n",
    "plt.title('Scatter Plot of Glucose Level vs. Age with Regression Line')\n",
    "plt.show()\n",
    "\n",
    "#Step 4:  Create a function which calculates the performance using R-squared using sklearn r2_score function.\n",
    "#         Your function input parameters are y (actual values) and y_hat (predicted).\n",
    "#         Your function returns the R-squared value\n",
    "#         Hint:  see https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html\n",
    "def simpleLRscore(y, yhat):\n",
    "# YOUR CODE HERE...\n",
    "    return r2_score(y, yhat)\n",
    "\n",
    "r2 = simpleLRscore(Glucose, yhat)\n",
    "print('r2:', round(r2, 3))\n",
    "\n",
    "#Step 5:  Run the doctest module.  DO NOT modify any code below this line!\n",
    "import doctest\n",
    "\"\"\"\n",
    "  >>> print('b1:', round(simpleLRcoeffsManual(Age, Glucose)[1], 3))\n",
    "  b1: 0.354\n",
    "  >>> print('b0:', round(simpleLRcoeffsManual(Age, Glucose)[0], 3))\n",
    "  b0: 67.348\n",
    "  >>> print('r2:', round(simpleLRscore(Glucose, yhat), 3))\n",
    "  r2: 0.357\n",
    "\"\"\"\n",
    "\n",
    "doctest.testmod()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gpl29vJWzHPJ"
   },
   "source": [
    "#**PART 2**:  Simple Linear Regression using Scikit learn\n",
    "\n",
    "In this project, you will experiment with the functions used to train a simple linear regression model using the Scikit learn library.\n",
    "Please reference the video lectures on Simple and Multiple Linear Regression for a description of this example and the main functions you will need in the implementation.\n",
    "Please complete the following steps in your Colab Script.  The reference script below provides template code and hints to help with each step.  \n",
    "\n",
    "-  **Step 1**:  Calculate the coefficients for a simple linear regression model using the appropriate sklearn functions (see the code below for hints).\n",
    "-  **Step 2**:  Create a scatter plot of the input dataset and generated regression line on the same plot. \n",
    "-  **Step 3**:  Calculate the performance using R-squared using sklearn r2_score function. \n",
    "-  **Step 4**:  Run the cell and confirm the coefficients and R-squared using the embedded doctest module.  \n",
    "-  **Step 5**:  Reflect on the performance of your model.  Confirm your outputs are the same as from Part 1, are they the same and should they be the same?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 131
    },
    "id": "kx3yiY9VIrgI",
    "outputId": "df20234f-4662-42a0-eb28-611255cbf173"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b1: 0.3539124057393234\n",
      "b0: 67.34791357165692\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.3573300148076213\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TestResults(failed=0, attempted=3)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "\n",
    "#Input Parameters\n",
    "Age = np.array([43,21,25,42,57,59,35,15,55,50,65,10,45,35])\n",
    "Glucose = np.array([99,65,79,75,87,81,80,80,90,70,95,67,90,82])\n",
    "AgeReshaped = Age.T.reshape(-1,1)   #T=transpose, -1=unknown rows, 1=col \n",
    "GlucoseReshaped = Glucose.T.reshape(-1,1) \n",
    "\n",
    "\n",
    "#Step 2:  Create a function which calculates the coefficients for a simple linear regression model \n",
    "#         Your function must input numpy arrays for the x and y variables and return coefficients b0,b1, and Yhat (predicted output).\n",
    "#         Your function MUST use the sklearn functions.\n",
    "#         Hint: Construct a linear regression object, then \"fit\" the data:\n",
    "#             https://scikit-learn.org/0.18/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression.fit\n",
    "#             https://scikit-learn.org/0.18/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression.predict\n",
    "#         \n",
    "#         \n",
    "def simpleLRcoeffsSkLearn(X, Y):\n",
    "#Return the tuple: b1, b0, Yhat in this order \n",
    "#Note - you must return your outputs in the order above in order to pass the doctest module.\n",
    "# YOUR CODE HERE...\n",
    "    model = LinearRegression()\n",
    "    model.fit(X, Y)\n",
    "    b1 = model.coef_\n",
    "    b0 = model.intercept_\n",
    "    Yhat = model.predict(X)\n",
    "    return b1, b0, Yhat\n",
    "\n",
    "b1, b0, Yhat = simpleLRcoeffsSkLearn(AgeReshaped, GlucoseReshaped)\n",
    "print('b1:', b1[0][0])\n",
    "print('b0:', b0[0])\n",
    "    \n",
    "#Step 3:  Create a scatter plot of the input dataset and generated regression line on the same plot.\n",
    "#         Hint: make sure you transpose and reshape yhat.  (yhat.T.reshape(-1,1))\n",
    "# YOUR CODE HERE...\n",
    "Yhat=Yhat.T.reshape(-1,1)\n",
    "plt.scatter(Age, Glucose)\n",
    "plt.plot(Age, Yhat, color='red')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Glucose Level')\n",
    "plt.title('Scatter Plot of Glucose Level vs. Age with Regression Line')\n",
    "plt.show()\n",
    "\n",
    "#Step 4:  Calculate the performance using R-squared using sklearn r2_score function.\n",
    "def simpleLRscoreSkLearn(y, yhat):\n",
    "# YOUR CODE HERE...\n",
    "    return r2_score(y, yhat)\n",
    "\n",
    "r2 = simpleLRscoreSkLearn(Glucose, Yhat)\n",
    "\n",
    "print('r2:', r2)\n",
    "\n",
    "#Step 5:  Run the doctest module.  DO NOT modify any code below this line!\n",
    "\n",
    "import doctest\n",
    "\"\"\"\n",
    "  >>> print('b1:', simpleLRcoeffsSkLearn(AgeReshaped, GlucoseReshaped)[0][0])\n",
    "  b1: [0.35391241]\n",
    "  >>> print('b0:', simpleLRcoeffsSkLearn(AgeReshaped, GlucoseReshaped)[1][0])\n",
    "  b0: 67.34791357165692\n",
    "  >>> print('r2:', simpleLRscoreSkLearn(GlucoseReshaped, simpleLRcoeffsSkLearn(AgeReshaped, GlucoseReshaped)[2]))\n",
    "  r2: 0.3573300148076213\n",
    "\"\"\"\n",
    "\n",
    "doctest.testmod()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f4rBpbqCItcC"
   },
   "source": [
    "#**PART 3**: Multiple Linear Regression\n",
    "In this project, you will expand on the example provided in the lecture train a multiple linear regression model using Pandas and the Scikit learn library and determine which independent variables have the greatest effect on the model performance.\n",
    "Please reference the video lectures on Simple and Multiple Linear Regression for a description of this example and the main functions you will need in the implementation.  The Multiple Linear Regression equation is shown here for reference:  \n",
    "\n",
    "$$\\hat y = b_0+b_1x_1+b_2x_2=B^T\\cdot X$$   \n",
    "\n",
    "Please complete the following steps in your Colab Script.  The reference script below provides template code and hints to help with each step.  \n",
    "\n",
    "-  **Step 1**:  Input the Stock Market dataset into a Pandas dataframe.\n",
    "-  **Step 2**:  Examine the relationship between the variables\n",
    "-  **Step 3**:  Write a function which performs Multiple Linear Regression (MLR) on the input dataset.\n",
    "-  **Step 4**:  Fit the regression model for ALL combinations of indep vars\n",
    "-  **Step 5**:  Predict the stock index price using your top 2 models from the test cases shown in the code cell below (i.e. test your top 2 models with the best r2 scores).\n",
    "-  **Step 6**: Record your regression equation - you will need to upload this to the accompanying assignment. Reflect on your results.  What combination of independent variables gave the best performing model?  Note that the more variable you use in your model, the more memory and processing resources you will need so does it make sense to use all of your independent variables if a subset is close in performance?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "2tG2s0iqRpla"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Year     Month  Interest_Rate  Unemployment_Rate        GDP  \\\n",
      "count    36.000000  36.00000      36.000000          36.000000  36.000000   \n",
      "mean   2017.000000   6.50000       2.183333           5.891667  20.450000   \n",
      "std       0.828079   3.50102       0.351308           0.175507   1.053565   \n",
      "min    2016.000000   1.00000       1.600000           5.600000  18.700000   \n",
      "25%    2016.000000   3.75000       1.892500           5.745000  19.575000   \n",
      "50%    2017.000000   6.50000       2.185000           5.890000  20.450000   \n",
      "75%    2018.000000   9.25000       2.477500           6.035000  21.325000   \n",
      "max    2018.000000  12.00000       2.770000           6.180000  22.200000   \n",
      "\n",
      "       Stock_Index_Price  \n",
      "count          36.000000  \n",
      "mean         1065.944444  \n",
      "std           178.332746  \n",
      "min           704.000000  \n",
      "25%           955.750000  \n",
      "50%          1054.500000  \n",
      "75%          1180.000000  \n",
      "max          1464.000000  \n",
      "   Year  Month  Interest_Rate  Unemployment_Rate   GDP  Stock_Index_Price\n",
      "0  2018     12           2.73               5.80  22.2               1464\n",
      "1  2018     11           2.70               5.75  20.6               1394\n",
      "2  2018     10           2.63               5.68  21.7               1357\n",
      "3  2018      9           2.40               5.65  19.7               1293\n",
      "4  2018      8           2.37               5.63  22.1               1256\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Case: ['Interest_Rate']\n",
      "Intercept: 89.49634225391299\n",
      "Coefficients: [447.22813841]\n",
      "Case: ['Unemployment_Rate']\n",
      "Intercept: 5872.544739201681\n",
      "Coefficients: [-815.83031877]\n",
      "Case: ['GDP']\n",
      "Intercept: -752.8159159159161\n",
      "Coefficients: [88.93693694]\n",
      "Case: ['Interest_Rate', 'Unemployment_Rate']\n",
      "Intercept: 2197.7528706602875\n",
      "Coefficients: [ 327.09060652 -313.31647815]\n",
      "Case: ['Interest_Rate', 'GDP']\n",
      "Intercept: -288.4747407568698\n",
      "Coefficients: [415.47404803  21.87290205]\n",
      "Case: ['Unemployment_Rate', 'GDP']\n",
      "Intercept: 4425.8357558077205\n",
      "Coefficients: [-716.66435407   42.17389607]\n",
      "Case: ['Interest_Rate', 'Unemployment_Rate', 'GDP']\n",
      "Intercept: 1802.3978378130578\n",
      "Coefficients: [ 299.610547   -307.27216408   20.52528504]\n",
      "Test Case: {'Interest_Rate': 2.75, 'Unemployment_Rate': 5.3, 'GDP': 20.0}\n",
      "Case 1 Prediction: 1436.674704376127\n",
      "Case 2 Prediction: 1291.5369324065796\n",
      "\n",
      "Test Case: {'Interest_Rate': 2.9, 'Unemployment_Rate': 5.9, 'GDP': 21.0}\n",
      "Case 1 Prediction: 1297.7484084626724\n",
      "Case 2 Prediction: 1375.7309416651824\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Multiple Linear Regression\n",
    "from pandas import DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "import statsmodels.api as sm\n",
    "\n",
    "Stock_Market = {'Year':[2018,2018,2018,2018,2018,2018,2018,2018,2018,2018,2018,2018,\\\n",
    "                         2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,\\\n",
    "                         2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016],\\\n",
    "                'Month':[12,11,10,9,8,7,6,5,4,3,2,1, 12,11,10,9,8,7,6,5,4,3,2,1, 12,11,10,9,8,7,6,5,4,3,2,1],\\\n",
    "                'Interest_Rate':[2.73, 2.7,  2.63, 2.4,  2.37, 2.77, 2.53, 2.23, 2.67, 2.6,  2.43, 2.2,  2.5,  2.47,\\\n",
    "                                 2.03, 2.33, 2.3,  2.57, 2.27, 2.1,  2.,   2.13, 2.07, 2.17, 1.87, 1.73, 1.67, 1.97,\\\n",
    "                                 1.93, 1.9,  1.83, 1.8,  1.77, 1.6,  1.7,  1.63],\\\n",
    "                'Unemployment_Rate':[5.8,  5.75, 5.68, 5.65, 5.63, 5.62, 5.72, 5.83, 5.78, 5.73, 5.7,  5.6,  6.08, 5.67,\\\n",
    "                                     5.97, 5.95, 5.88, 5.87, 5.85, 5.82, 5.77, 5.98, 5.93, 5.92, 5.9,  6.07, 6.05, 6.,\\\n",
    "                                     6.03, 6.02, 6.15, 6.18, 6.17, 6.12, 6.13, 6.1],\\\n",
    "                'GDP':[22.2, 20.6, 21.7, 19.7, 22.1, 19.5, 18.7, 22.,  21.6, 19.4, 21.9, 21.8,\\\n",
    "                       21.5, 21.2, 21.1, 21.,  20.5, 21.4, 20.9, 20.4, 20.,  20.8, 20.3, 20.1,\\\n",
    "                       19.8, 19.6, 20.2, 19.3, 20.7, 18.9, 21.3, 19.2, 19.,  18.8, 19.9, 19.1],\\\n",
    "                'Stock_Index_Price': [1464,1394,1357,1293,1256,1254,1234,1230,1195,1175,1167,1159,\\\n",
    "                                      1147,1130,1075,1071,1065,1058,1051,1049,1043, 984, 976, 971,\\\n",
    "                                       968, 965, 958, 949, 943, 922, 884, 876, 866, 822, 719, 704]\n",
    "                }\n",
    "\n",
    "#Step 1:  Input the Stock Market dataset into a Pandas dataframe.\n",
    "#         Print out a description of the data.\n",
    "#         Hint: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html\n",
    "# YOUR CODE HERE... \n",
    "df = DataFrame(Stock_Market, columns=['Year', 'Month', 'Interest_Rate', 'Unemployment_Rate', 'GDP', 'Stock_Index_Price'])\n",
    "print(df.describe())\n",
    "print(df.head())\n",
    "#Step 2:  Examine the relationship between the variables\n",
    "#         Plot each of the independent variables vs. Stock_Index_Price using Matplotlib\n",
    "#         Which independent vars do you think will have the greatest impact on the dep variable?\n",
    "# YOUR CODE HERE...\n",
    "fig, axs = plt.subplots(2, 2, figsize=(12, 8))\n",
    "axs[0, 0].scatter(df['Interest_Rate'], df['Stock_Index_Price'])\n",
    "axs[0, 0].set_xlabel('Interest Rate')\n",
    "axs[0, 0].set_ylabel('Stock Index Price')\n",
    "axs[0, 1].scatter(df['Unemployment_Rate'], df['Stock_Index_Price'])\n",
    "axs[0, 1].set_xlabel('Unemployment Rate')\n",
    "axs[0, 1].set_ylabel('Stock Index Price')\n",
    "axs[1, 0].scatter(df['GDP'], df['Stock_Index_Price'])\n",
    "axs[1, 0].set_xlabel('GDP')\n",
    "axs[1, 0].set_ylabel('Stock Index Price')\n",
    "axs[1, 1].scatter(df['Year'], df['Stock_Index_Price'])\n",
    "axs[1, 1].set_xlabel('Year')\n",
    "axs[1, 1].set_ylabel('Stock Index Price')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "#Step 3:  Write a function which performs Multiple Linear Regression (MLR) on the input dataset.\n",
    "#         Use sklearn to perform the MLR and determine the intercept and coefficents.\n",
    "#         Your function must input parameters X, Y for the indep variable and dependent variable respectively.\n",
    "#         Your function must return the intercept, coefficients, and score in this exact order.\n",
    "#         Hint:  https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html\n",
    "#         Hint:  https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression.fit\n",
    "#         Hint:  https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression.predict\n",
    "#         Hint:  https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html\n",
    "def MLR(X,Y):\n",
    "# YOUR CODE HERE...\n",
    "    model = linear_model.LinearRegression()\n",
    "    model.fit(X, Y)\n",
    "    intercept = model.intercept_\n",
    "    coefficients = model.coef_\n",
    "    score = model.score(X, Y)\n",
    "    return intercept, coefficients, score\n",
    "\n",
    "\n",
    "#Step 4:  Fit the regression model for ALL combinations of indep vars\n",
    "#         Call the function you created in Step 3.\n",
    "#         Print out the intercept, coefficients and r2 score for each of the cases.\n",
    "#         Hint:  Extract the X parameter from the dataframe.\n",
    "#         Hint:  The Y parameter will be Stock_Index_Price for all cases.\n",
    "#Case 1:  Interest Rate only\n",
    "#Case 2:  Unemployment Rate only\n",
    "#Case 3:  GDP only\n",
    "#Case 4:  Interest Rate + Unemployment Rate\n",
    "#Case 5:  Interest Rate + GDP\n",
    "#Case 6:  Unemployment Rate + GDP\n",
    "#Case 7:  Interest Rate + Unemployment Rate + GDP\n",
    "# YOUR CODE HERE...\n",
    "\n",
    "X = df[['Interest_Rate', 'Unemployment_Rate', 'GDP']]\n",
    "Y = df['Stock_Index_Price']\n",
    "\n",
    "cases = [\n",
    "    ['Interest_Rate'],\n",
    "    ['Unemployment_Rate'],\n",
    "    ['GDP'],\n",
    "    ['Interest_Rate', 'Unemployment_Rate'],\n",
    "    ['Interest_Rate', 'GDP'],\n",
    "    ['Unemployment_Rate', 'GDP'],\n",
    "    ['Interest_Rate', 'Unemployment_Rate', 'GDP']\n",
    "]\n",
    "\n",
    "for case in cases:\n",
    "    X_case = df[case]\n",
    "    intercept, coefficients, score = MLR(X_case, Y)\n",
    "    print('Case:', case)\n",
    "    print('Intercept:', intercept)\n",
    "    print('Coefficients:', coefficients)\n",
    "    \n",
    "\n",
    "#Step 5:  Predict the stock index price using your top 2 models with the following test cases (i.e. the top 2 models with the best r2 scores):\n",
    "#         You should regenerate your model in case it was overwritten in the previous step when testing the other conditions.\n",
    "#         Set the values for each of the independent variables, then call the sklearn predict function.\n",
    "#         Hint:  https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression.predict\n",
    "#         Test case 1:  Predict the stock index price based on the following:\n",
    "#                       New_Interest_Rate = 2.75\n",
    "#                       New_Unemployment_Rate = 5.3\n",
    "#                       New_GDP = 20.0\n",
    "#         Test case 2:  Predict the stock index price based on the following:\n",
    "#                       New_Interest_Rate = 2.9\n",
    "#                       New_Unemployment_Rate = 5.9\n",
    "#                       New_GDP = 21.0\n",
    "# NOTE - **** if your model does not use all 3 of the parameters given in the test cases above, then just leave them out **** \n",
    "# YOUR CODE HERE...\n",
    "\n",
    "test_cases = [\n",
    "    {'Interest_Rate': 2.75, 'Unemployment_Rate': 5.3, 'GDP': 20.0},\n",
    "    {'Interest_Rate': 2.9, 'Unemployment_Rate': 5.9, 'GDP': 21.0}\n",
    "]\n",
    "\n",
    "# Generate the models for the top 2 cases\n",
    "X_case1 = df[['Interest_Rate', 'Unemployment_Rate']]\n",
    "X_case2 = df[['Interest_Rate', 'GDP']]\n",
    "Y = df['Stock_Index_Price']\n",
    "intercept1, coefficients1, _ = MLR(X_case1, Y)\n",
    "intercept2, coefficients2, _ = MLR(X_case2, Y)\n",
    "\n",
    "# Predict the stock index price for the test cases\n",
    "for test_case in test_cases:\n",
    "    case1_prediction = intercept1 + coefficients1[0] * test_case['Interest_Rate'] + coefficients1[1] * test_case['Unemployment_Rate']\n",
    "    case2_prediction = intercept2 + coefficients2[0] * test_case['Interest_Rate'] + coefficients2[1] * test_case['GDP']\n",
    "    print('Test Case:', test_case)\n",
    "    print('Case 1 Prediction:', case1_prediction)\n",
    "    print('Case 2 Prediction:', case2_prediction)\n",
    "    print()\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EvK2ywSxBmuW"
   },
   "source": [
    "#**PART 4**:  Multiple Linear Regression using Actual Data\n",
    "In this section you will continue the analysis from Part 3 but this time you will search and find ACTUAL data from 2018-2020 based on at least 4 of the following independent variables:\n",
    "- **Unemployment:** https://www.bls.gov/charts/employment-situation/civilian-unemployment-rate.htm\n",
    "- **Effective Federal Funds rate:**  https://fred.stlouisfed.org/series/FEDFUNDS (fedfunds.csv in downloads)\n",
    "- **Average Temperature in NYC:**  https://www.weather.gov/media/okx/Climate/CentralPark/monthlyannualtemp.pdf.  \n",
    "\n",
    "and/or find some on your own, for example search for one of the following during the time period:\n",
    "- Savings rate\n",
    "- Personal income\n",
    "- Average home price in NYC\n",
    "- Inflation rate\n",
    "- Birth rate\n",
    "- Others? your choice as long as they are appropriate and relevant!\n",
    "\n",
    "----- Procedure ------\n",
    "- **Step 1**:  Find at least 3 ACTUAL datasets representing independent variables that might affect the stock index price between the years 2018-2020 (36 months total).  Your data should be in monthly increments.\n",
    "- **Step 2**:  Input the data to your script.  Use Part 3 in this project as a guide.\n",
    "- **Step 3**:  Examine the relationship between the variables.  If linear relationships exist, they should be a good candidate variable for your model.\n",
    "- **Step 4**:  Fit the regression model for ALL combinations of indep vars.\n",
    "- **Step 5**:  Predict the stock index price using your top 2 models with at least 2 test cases (you decide the independent variable values you would like to test with.\n",
    "-  **Step 6**:  Record your regression equation - you will need to upload this to the accompanying assignment.  Reflect on your results.  What combination of independent variables gave the best performing model?  Did you find a subset of independent variables that performed well enough such that you don't need to consume the memory and processing required to include all of them?  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step1:Finding 3 Actual Datasets representing independent variables affecting the stock index price between the years 2018-2020 (36 months total). The data is in monthly increments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "I_7WZdyUE7KI"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year\n",
       "0   2018\n",
       "1   2018\n",
       "2   2018\n",
       "3   2018\n",
       "4   2018\n",
       "5   2018\n",
       "6   2018\n",
       "7   2018\n",
       "8   2018\n",
       "9   2018\n",
       "10  2018\n",
       "11  2018\n",
       "12  2019\n",
       "13  2019\n",
       "14  2019\n",
       "15  2019\n",
       "16  2019\n",
       "17  2019\n",
       "18  2019\n",
       "19  2019\n",
       "20  2019\n",
       "21  2019\n",
       "22  2019\n",
       "23  2019\n",
       "24  2020\n",
       "25  2020\n",
       "26  2020\n",
       "27  2020\n",
       "28  2020\n",
       "29  2020\n",
       "30  2020\n",
       "31  2020\n",
       "32  2020\n",
       "33  2020\n",
       "34  2020\n",
       "35  2020"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#YOUR CODE HERE...\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import statsmodels.api as sm\n",
    "import itertools\n",
    "\n",
    "# Step1 Import the data\n",
    "\n",
    "unemployment = pd.read_csv('./unemployment.csv', usecols = ['Unemployment_Rate'])\n",
    "\n",
    "federal_funds_rate = pd.read_csv('./federal_funds_rate.csv', usecols = ['Interest_Rate'])\n",
    "\n",
    "#https://www.weather.gov/wrh/climate?wfo=okx  \n",
    "#used same data as in the pdf file link above for years 2018-2020, from the weather.gov url that was available in csv format\n",
    "\n",
    "average_temperature_nyc = pd.read_csv('./average_temperature_nyc.csv', usecols=['Temperature'])\n",
    "\n",
    "#data obtained from \n",
    "#https://finance.yahoo.com/quote/%5EGSPC/history?period1=1515542400&period2=1609372800&interval=1mo&filt, er=history&frequency=1mo&includeAdjustedClose=true\n",
    "\n",
    "stock_index_price = pd.read_csv('./stock_index_price.csv', usecols=['stock_index_price'])\n",
    "\n",
    "#df = pd.concat([unemployment, federal_funds_rate, average_temperature_nyc, stock_index_price], axis=1)\n",
    "\n",
    "Stock_Market = {'Year':[2018,2018,2018,2018,2018,2018,2018,2018,2018,2018,2018,2018,\\\n",
    "                         2019,2019,2019,2019,2019,2019,2019,2019,2019,2019,2019,2019,\\\n",
    "                         2020,2020,2020,2020,2020,2020,2020,2020,2020,2020,2020,2020]}\n",
    "\n",
    "df1 = pd.DataFrame(Stock_Market, columns=['Year'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>stock_index_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.41</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.7</td>\n",
       "      <td>2713.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.42</td>\n",
       "      <td>4.1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2640.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>2648.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.69</td>\n",
       "      <td>4.0</td>\n",
       "      <td>49.5</td>\n",
       "      <td>2705.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.70</td>\n",
       "      <td>3.8</td>\n",
       "      <td>66.9</td>\n",
       "      <td>2718.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.82</td>\n",
       "      <td>4.0</td>\n",
       "      <td>71.7</td>\n",
       "      <td>2816.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.91</td>\n",
       "      <td>3.8</td>\n",
       "      <td>77.6</td>\n",
       "      <td>2901.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.91</td>\n",
       "      <td>3.8</td>\n",
       "      <td>78.1</td>\n",
       "      <td>2913.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.95</td>\n",
       "      <td>3.7</td>\n",
       "      <td>70.7</td>\n",
       "      <td>2711.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.19</td>\n",
       "      <td>3.8</td>\n",
       "      <td>57.7</td>\n",
       "      <td>2760.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.20</td>\n",
       "      <td>3.8</td>\n",
       "      <td>44.4</td>\n",
       "      <td>2506.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.27</td>\n",
       "      <td>3.9</td>\n",
       "      <td>40.1</td>\n",
       "      <td>2704.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.40</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>2784.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.40</td>\n",
       "      <td>3.8</td>\n",
       "      <td>36.2</td>\n",
       "      <td>2834.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.41</td>\n",
       "      <td>3.8</td>\n",
       "      <td>41.7</td>\n",
       "      <td>2945.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.42</td>\n",
       "      <td>3.6</td>\n",
       "      <td>55.5</td>\n",
       "      <td>2752.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.39</td>\n",
       "      <td>3.7</td>\n",
       "      <td>62.2</td>\n",
       "      <td>2941.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.38</td>\n",
       "      <td>3.6</td>\n",
       "      <td>71.7</td>\n",
       "      <td>2980.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.40</td>\n",
       "      <td>3.7</td>\n",
       "      <td>79.6</td>\n",
       "      <td>2926.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.13</td>\n",
       "      <td>3.7</td>\n",
       "      <td>75.5</td>\n",
       "      <td>2976.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.04</td>\n",
       "      <td>3.5</td>\n",
       "      <td>70.4</td>\n",
       "      <td>3037.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.83</td>\n",
       "      <td>3.6</td>\n",
       "      <td>59.9</td>\n",
       "      <td>3140.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.55</td>\n",
       "      <td>3.6</td>\n",
       "      <td>43.9</td>\n",
       "      <td>3230.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.55</td>\n",
       "      <td>3.6</td>\n",
       "      <td>38.3</td>\n",
       "      <td>3225.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.55</td>\n",
       "      <td>3.5</td>\n",
       "      <td>39.1</td>\n",
       "      <td>2954.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.58</td>\n",
       "      <td>3.5</td>\n",
       "      <td>40.1</td>\n",
       "      <td>2584.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.65</td>\n",
       "      <td>4.4</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2912.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.05</td>\n",
       "      <td>14.7</td>\n",
       "      <td>50.4</td>\n",
       "      <td>3044.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.05</td>\n",
       "      <td>13.2</td>\n",
       "      <td>60.3</td>\n",
       "      <td>3100.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.08</td>\n",
       "      <td>11.0</td>\n",
       "      <td>73.7</td>\n",
       "      <td>3271.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.09</td>\n",
       "      <td>10.2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3500.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.10</td>\n",
       "      <td>8.4</td>\n",
       "      <td>76.9</td>\n",
       "      <td>3363.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.09</td>\n",
       "      <td>7.9</td>\n",
       "      <td>68.8</td>\n",
       "      <td>3269.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.09</td>\n",
       "      <td>6.9</td>\n",
       "      <td>57.9</td>\n",
       "      <td>3621.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.09</td>\n",
       "      <td>6.7</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3756.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.09</td>\n",
       "      <td>6.7</td>\n",
       "      <td>39.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Interest_Rate  Unemployment_Rate  Temperature stock_index_price\n",
       "0            1.41                4.0         31.7           2713.83\n",
       "1            1.42                4.1         42.0           2640.87\n",
       "2            1.51                4.0         40.1           2648.05\n",
       "3            1.69                4.0         49.5           2705.27\n",
       "4            1.70                3.8         66.9           2718.37\n",
       "5            1.82                4.0         71.7           2816.29\n",
       "6            1.91                3.8         77.6           2901.52\n",
       "7            1.91                3.8         78.1           2913.98\n",
       "8            1.95                3.7         70.7           2711.74\n",
       "9            2.19                3.8         57.7           2760.17\n",
       "10           2.20                3.8         44.4           2506.85\n",
       "11           2.27                3.9         40.1           2704.10\n",
       "12           2.40                4.0         32.5           2784.49\n",
       "13           2.40                3.8         36.2           2834.40\n",
       "14           2.41                3.8         41.7           2945.83\n",
       "15           2.42                3.6         55.5           2752.06\n",
       "16           2.39                3.7         62.2           2941.76\n",
       "17           2.38                3.6         71.7           2980.38\n",
       "18           2.40                3.7         79.6           2926.46\n",
       "19           2.13                3.7         75.5           2976.74\n",
       "20           2.04                3.5         70.4           3037.56\n",
       "21           1.83                3.6         59.9           3140.98\n",
       "22           1.55                3.6         43.9           3230.78\n",
       "23           1.55                3.6         38.3           3225.52\n",
       "24           1.55                3.5         39.1           2954.22\n",
       "25           1.58                3.5         40.1           2584.59\n",
       "26           0.65                4.4         48.0           2912.43\n",
       "27           0.05               14.7         50.4           3044.31\n",
       "28           0.05               13.2         60.3           3100.29\n",
       "29           0.08               11.0         73.7           3271.12\n",
       "30           0.09               10.2         80.0           3500.31\n",
       "31           0.10                8.4         76.9           3363.00\n",
       "32           0.09                7.9         68.8           3269.96\n",
       "33           0.09                6.9         57.9           3621.63\n",
       "34           0.09                6.7         53.0           3756.07\n",
       "35           0.09                6.7         39.2               NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.concat([federal_funds_rate, unemployment,average_temperature_nyc, stock_index_price], axis=1)\n",
    "\n",
    "df2\n",
    "#clean up the commas in the  stock_index_price column  \n",
    "\n",
    "df2[\"stock_index_price\"] = df2[\"stock_index_price\"].str.replace(',','', regex=True)\n",
    " \n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>stock_index_price</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.41</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.7</td>\n",
       "      <td>2713.83</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.42</td>\n",
       "      <td>4.1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2640.87</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>2648.05</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.69</td>\n",
       "      <td>4.0</td>\n",
       "      <td>49.5</td>\n",
       "      <td>2705.27</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.70</td>\n",
       "      <td>3.8</td>\n",
       "      <td>66.9</td>\n",
       "      <td>2718.37</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.82</td>\n",
       "      <td>4.0</td>\n",
       "      <td>71.7</td>\n",
       "      <td>2816.29</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.91</td>\n",
       "      <td>3.8</td>\n",
       "      <td>77.6</td>\n",
       "      <td>2901.52</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.91</td>\n",
       "      <td>3.8</td>\n",
       "      <td>78.1</td>\n",
       "      <td>2913.98</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.95</td>\n",
       "      <td>3.7</td>\n",
       "      <td>70.7</td>\n",
       "      <td>2711.74</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.19</td>\n",
       "      <td>3.8</td>\n",
       "      <td>57.7</td>\n",
       "      <td>2760.17</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.20</td>\n",
       "      <td>3.8</td>\n",
       "      <td>44.4</td>\n",
       "      <td>2506.85</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.27</td>\n",
       "      <td>3.9</td>\n",
       "      <td>40.1</td>\n",
       "      <td>2704.10</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.40</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>2784.49</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.40</td>\n",
       "      <td>3.8</td>\n",
       "      <td>36.2</td>\n",
       "      <td>2834.40</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.41</td>\n",
       "      <td>3.8</td>\n",
       "      <td>41.7</td>\n",
       "      <td>2945.83</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.42</td>\n",
       "      <td>3.6</td>\n",
       "      <td>55.5</td>\n",
       "      <td>2752.06</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.39</td>\n",
       "      <td>3.7</td>\n",
       "      <td>62.2</td>\n",
       "      <td>2941.76</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.38</td>\n",
       "      <td>3.6</td>\n",
       "      <td>71.7</td>\n",
       "      <td>2980.38</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.40</td>\n",
       "      <td>3.7</td>\n",
       "      <td>79.6</td>\n",
       "      <td>2926.46</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.13</td>\n",
       "      <td>3.7</td>\n",
       "      <td>75.5</td>\n",
       "      <td>2976.74</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.04</td>\n",
       "      <td>3.5</td>\n",
       "      <td>70.4</td>\n",
       "      <td>3037.56</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.83</td>\n",
       "      <td>3.6</td>\n",
       "      <td>59.9</td>\n",
       "      <td>3140.98</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.55</td>\n",
       "      <td>3.6</td>\n",
       "      <td>43.9</td>\n",
       "      <td>3230.78</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.55</td>\n",
       "      <td>3.6</td>\n",
       "      <td>38.3</td>\n",
       "      <td>3225.52</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.55</td>\n",
       "      <td>3.5</td>\n",
       "      <td>39.1</td>\n",
       "      <td>2954.22</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.58</td>\n",
       "      <td>3.5</td>\n",
       "      <td>40.1</td>\n",
       "      <td>2584.59</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.65</td>\n",
       "      <td>4.4</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2912.43</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.05</td>\n",
       "      <td>14.7</td>\n",
       "      <td>50.4</td>\n",
       "      <td>3044.31</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.05</td>\n",
       "      <td>13.2</td>\n",
       "      <td>60.3</td>\n",
       "      <td>3100.29</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.08</td>\n",
       "      <td>11.0</td>\n",
       "      <td>73.7</td>\n",
       "      <td>3271.12</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.09</td>\n",
       "      <td>10.2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3500.31</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.10</td>\n",
       "      <td>8.4</td>\n",
       "      <td>76.9</td>\n",
       "      <td>3363.00</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.09</td>\n",
       "      <td>7.9</td>\n",
       "      <td>68.8</td>\n",
       "      <td>3269.96</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.09</td>\n",
       "      <td>6.9</td>\n",
       "      <td>57.9</td>\n",
       "      <td>3621.63</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.09</td>\n",
       "      <td>6.7</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3756.07</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.09</td>\n",
       "      <td>6.7</td>\n",
       "      <td>39.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Interest_Rate  Unemployment_Rate  Temperature stock_index_price  Year\n",
       "0            1.41                4.0         31.7           2713.83  2018\n",
       "1            1.42                4.1         42.0           2640.87  2018\n",
       "2            1.51                4.0         40.1           2648.05  2018\n",
       "3            1.69                4.0         49.5           2705.27  2018\n",
       "4            1.70                3.8         66.9           2718.37  2018\n",
       "5            1.82                4.0         71.7           2816.29  2018\n",
       "6            1.91                3.8         77.6           2901.52  2018\n",
       "7            1.91                3.8         78.1           2913.98  2018\n",
       "8            1.95                3.7         70.7           2711.74  2018\n",
       "9            2.19                3.8         57.7           2760.17  2018\n",
       "10           2.20                3.8         44.4           2506.85  2018\n",
       "11           2.27                3.9         40.1           2704.10  2018\n",
       "12           2.40                4.0         32.5           2784.49  2019\n",
       "13           2.40                3.8         36.2           2834.40  2019\n",
       "14           2.41                3.8         41.7           2945.83  2019\n",
       "15           2.42                3.6         55.5           2752.06  2019\n",
       "16           2.39                3.7         62.2           2941.76  2019\n",
       "17           2.38                3.6         71.7           2980.38  2019\n",
       "18           2.40                3.7         79.6           2926.46  2019\n",
       "19           2.13                3.7         75.5           2976.74  2019\n",
       "20           2.04                3.5         70.4           3037.56  2019\n",
       "21           1.83                3.6         59.9           3140.98  2019\n",
       "22           1.55                3.6         43.9           3230.78  2019\n",
       "23           1.55                3.6         38.3           3225.52  2019\n",
       "24           1.55                3.5         39.1           2954.22  2020\n",
       "25           1.58                3.5         40.1           2584.59  2020\n",
       "26           0.65                4.4         48.0           2912.43  2020\n",
       "27           0.05               14.7         50.4           3044.31  2020\n",
       "28           0.05               13.2         60.3           3100.29  2020\n",
       "29           0.08               11.0         73.7           3271.12  2020\n",
       "30           0.09               10.2         80.0           3500.31  2020\n",
       "31           0.10                8.4         76.9           3363.00  2020\n",
       "32           0.09                7.9         68.8           3269.96  2020\n",
       "33           0.09                6.9         57.9           3621.63  2020\n",
       "34           0.09                6.7         53.0           3756.07  2020\n",
       "35           0.09                6.7         39.2               NaN  2020"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the Dataframe\n",
    "\n",
    "df=pd.concat([df2, df1.reindex(df1.index)], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Unemployment_Rate</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>stock_index_price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.41</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.7</td>\n",
       "      <td>2713.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.42</td>\n",
       "      <td>4.1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2640.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.51</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>2648.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.69</td>\n",
       "      <td>4.0</td>\n",
       "      <td>49.5</td>\n",
       "      <td>2705.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.70</td>\n",
       "      <td>3.8</td>\n",
       "      <td>66.9</td>\n",
       "      <td>2718.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.82</td>\n",
       "      <td>4.0</td>\n",
       "      <td>71.7</td>\n",
       "      <td>2816.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.91</td>\n",
       "      <td>3.8</td>\n",
       "      <td>77.6</td>\n",
       "      <td>2901.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.91</td>\n",
       "      <td>3.8</td>\n",
       "      <td>78.1</td>\n",
       "      <td>2913.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.95</td>\n",
       "      <td>3.7</td>\n",
       "      <td>70.7</td>\n",
       "      <td>2711.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2.19</td>\n",
       "      <td>3.8</td>\n",
       "      <td>57.7</td>\n",
       "      <td>2760.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2.20</td>\n",
       "      <td>3.8</td>\n",
       "      <td>44.4</td>\n",
       "      <td>2506.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2.27</td>\n",
       "      <td>3.9</td>\n",
       "      <td>40.1</td>\n",
       "      <td>2704.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2.40</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>2784.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2.40</td>\n",
       "      <td>3.8</td>\n",
       "      <td>36.2</td>\n",
       "      <td>2834.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2.41</td>\n",
       "      <td>3.8</td>\n",
       "      <td>41.7</td>\n",
       "      <td>2945.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2.42</td>\n",
       "      <td>3.6</td>\n",
       "      <td>55.5</td>\n",
       "      <td>2752.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2.39</td>\n",
       "      <td>3.7</td>\n",
       "      <td>62.2</td>\n",
       "      <td>2941.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2.38</td>\n",
       "      <td>3.6</td>\n",
       "      <td>71.7</td>\n",
       "      <td>2980.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2.40</td>\n",
       "      <td>3.7</td>\n",
       "      <td>79.6</td>\n",
       "      <td>2926.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2.13</td>\n",
       "      <td>3.7</td>\n",
       "      <td>75.5</td>\n",
       "      <td>2976.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2.04</td>\n",
       "      <td>3.5</td>\n",
       "      <td>70.4</td>\n",
       "      <td>3037.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1.83</td>\n",
       "      <td>3.6</td>\n",
       "      <td>59.9</td>\n",
       "      <td>3140.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1.55</td>\n",
       "      <td>3.6</td>\n",
       "      <td>43.9</td>\n",
       "      <td>3230.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1.55</td>\n",
       "      <td>3.6</td>\n",
       "      <td>38.3</td>\n",
       "      <td>3225.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1.55</td>\n",
       "      <td>3.5</td>\n",
       "      <td>39.1</td>\n",
       "      <td>2954.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1.58</td>\n",
       "      <td>3.5</td>\n",
       "      <td>40.1</td>\n",
       "      <td>2584.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.65</td>\n",
       "      <td>4.4</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2912.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.05</td>\n",
       "      <td>14.7</td>\n",
       "      <td>50.4</td>\n",
       "      <td>3044.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.05</td>\n",
       "      <td>13.2</td>\n",
       "      <td>60.3</td>\n",
       "      <td>3100.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.08</td>\n",
       "      <td>11.0</td>\n",
       "      <td>73.7</td>\n",
       "      <td>3271.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.09</td>\n",
       "      <td>10.2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3500.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.10</td>\n",
       "      <td>8.4</td>\n",
       "      <td>76.9</td>\n",
       "      <td>3363.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.09</td>\n",
       "      <td>7.9</td>\n",
       "      <td>68.8</td>\n",
       "      <td>3269.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.09</td>\n",
       "      <td>6.9</td>\n",
       "      <td>57.9</td>\n",
       "      <td>3621.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.09</td>\n",
       "      <td>6.7</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3756.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.09</td>\n",
       "      <td>6.7</td>\n",
       "      <td>39.2</td>\n",
       "      <td>2968.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Interest_Rate  Unemployment_Rate  Temperature  stock_index_price\n",
       "Year                                                                  \n",
       "2018           1.41                4.0         31.7            2713.83\n",
       "2018           1.42                4.1         42.0            2640.87\n",
       "2018           1.51                4.0         40.1            2648.05\n",
       "2018           1.69                4.0         49.5            2705.27\n",
       "2018           1.70                3.8         66.9            2718.37\n",
       "2018           1.82                4.0         71.7            2816.29\n",
       "2018           1.91                3.8         77.6            2901.52\n",
       "2018           1.91                3.8         78.1            2913.98\n",
       "2018           1.95                3.7         70.7            2711.74\n",
       "2018           2.19                3.8         57.7            2760.17\n",
       "2018           2.20                3.8         44.4            2506.85\n",
       "2018           2.27                3.9         40.1            2704.10\n",
       "2019           2.40                4.0         32.5            2784.49\n",
       "2019           2.40                3.8         36.2            2834.40\n",
       "2019           2.41                3.8         41.7            2945.83\n",
       "2019           2.42                3.6         55.5            2752.06\n",
       "2019           2.39                3.7         62.2            2941.76\n",
       "2019           2.38                3.6         71.7            2980.38\n",
       "2019           2.40                3.7         79.6            2926.46\n",
       "2019           2.13                3.7         75.5            2976.74\n",
       "2019           2.04                3.5         70.4            3037.56\n",
       "2019           1.83                3.6         59.9            3140.98\n",
       "2019           1.55                3.6         43.9            3230.78\n",
       "2019           1.55                3.6         38.3            3225.52\n",
       "2020           1.55                3.5         39.1            2954.22\n",
       "2020           1.58                3.5         40.1            2584.59\n",
       "2020           0.65                4.4         48.0            2912.43\n",
       "2020           0.05               14.7         50.4            3044.31\n",
       "2020           0.05               13.2         60.3            3100.29\n",
       "2020           0.08               11.0         73.7            3271.12\n",
       "2020           0.09               10.2         80.0            3500.31\n",
       "2020           0.10                8.4         76.9            3363.00\n",
       "2020           0.09                7.9         68.8            3269.96\n",
       "2020           0.09                6.9         57.9            3621.63\n",
       "2020           0.09                6.7         53.0            3756.07\n",
       "2020           0.09                6.7         39.2            2968.00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert the string objects in the stock_index_price column to float in the \n",
    "\n",
    "\n",
    "df['stock_index_price'] = df['stock_index_price'].astype(float)\n",
    "\n",
    "\n",
    "# replace the NAN in the stock_index_price column with mean value\n",
    "\n",
    "df['stock_index_price'].fillna(int(df['stock_index_price'].mean()), inplace=True)\n",
    "\n",
    "#set the Year column as the index column\n",
    "\n",
    "df.set_index('Year')\n",
    "#df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(12, 8))\n",
    "axs[0, 0].scatter(df['Interest_Rate'], df['stock_index_price'])\n",
    "axs[0, 0].set_xlabel('Interest Rate')\n",
    "axs[0, 0].set_ylabel('Stock Index Price')\n",
    "axs[0, 1].scatter(df['Unemployment_Rate'], df['stock_index_price'])\n",
    "axs[0, 1].set_xlabel('Unemployment Rate')\n",
    "axs[0, 1].set_ylabel('Stock Index Price')\n",
    "axs[1, 0].scatter(df['Temperature'], df['stock_index_price'])\n",
    "axs[1, 0].set_xlabel('Temperature')\n",
    "axs[1, 0].set_ylabel('Stock Index Price')\n",
    "axs[1, 1].scatter(df['Year'], df['stock_index_price'])\n",
    "axs[1, 1].set_xlabel('Year')\n",
    "axs[1, 1].set_ylabel('Stock Index Price')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'Interest_Rate':'IR', 'Unemployment_Rate':'UER', 'Temperature':'TEMP'}, inplace = True)\n",
    "df.to_csv('./CleanData.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         IR       UER      TEMP  stock_index_price      Year\n",
      "IR                 1.000000 -0.805034 -0.106685          -0.649005 -0.679457\n",
      "UER               -0.805034  1.000000  0.205482           0.499104  0.601308\n",
      "TEMP              -0.106685  0.205482  1.000000           0.327836  0.037425\n",
      "stock_index_price -0.649005  0.499104  0.327836           1.000000  0.666955\n",
      "Year              -0.679457  0.601308  0.037425           0.666955  1.000000\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv('./CleanData.csv')\n",
    "df.set_index('Year')\n",
    "# Examining the relationship between the variables using correlation\n",
    "print(df.corr())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=df.stock_index_price\n",
    "X=df.drop('stock_index_price',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 31773.44\n",
      "Variance score: 0.61\n"
     ]
    }
   ],
   "source": [
    "# Predicting Y for our X\n",
    "\n",
    "Y_pred = regr.predict(X)\n",
    "\n",
    "print(\"Mean squared error: %.2f\" % mean_squared_error(Y, Y_pred))\n",
    "print('Variance score: %.2f' % r2_score(Y, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Y = df['stock_index_price']\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['IR','UER','TEMP']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IR</th>\n",
       "      <th>UER</th>\n",
       "      <th>TEMP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.41</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.42</td>\n",
       "      <td>4.1</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.69</td>\n",
       "      <td>4.0</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.70</td>\n",
       "      <td>3.8</td>\n",
       "      <td>66.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.82</td>\n",
       "      <td>4.0</td>\n",
       "      <td>71.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.91</td>\n",
       "      <td>3.8</td>\n",
       "      <td>77.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.91</td>\n",
       "      <td>3.8</td>\n",
       "      <td>78.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.95</td>\n",
       "      <td>3.7</td>\n",
       "      <td>70.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.19</td>\n",
       "      <td>3.8</td>\n",
       "      <td>57.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.20</td>\n",
       "      <td>3.8</td>\n",
       "      <td>44.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.27</td>\n",
       "      <td>3.9</td>\n",
       "      <td>40.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.40</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.40</td>\n",
       "      <td>3.8</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.41</td>\n",
       "      <td>3.8</td>\n",
       "      <td>41.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.42</td>\n",
       "      <td>3.6</td>\n",
       "      <td>55.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.39</td>\n",
       "      <td>3.7</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.38</td>\n",
       "      <td>3.6</td>\n",
       "      <td>71.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.40</td>\n",
       "      <td>3.7</td>\n",
       "      <td>79.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.13</td>\n",
       "      <td>3.7</td>\n",
       "      <td>75.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.04</td>\n",
       "      <td>3.5</td>\n",
       "      <td>70.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.83</td>\n",
       "      <td>3.6</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.55</td>\n",
       "      <td>3.6</td>\n",
       "      <td>43.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.55</td>\n",
       "      <td>3.6</td>\n",
       "      <td>38.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.55</td>\n",
       "      <td>3.5</td>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.58</td>\n",
       "      <td>3.5</td>\n",
       "      <td>40.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.65</td>\n",
       "      <td>4.4</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.05</td>\n",
       "      <td>14.7</td>\n",
       "      <td>50.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.05</td>\n",
       "      <td>13.2</td>\n",
       "      <td>60.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.08</td>\n",
       "      <td>11.0</td>\n",
       "      <td>73.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.09</td>\n",
       "      <td>10.2</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.10</td>\n",
       "      <td>8.4</td>\n",
       "      <td>76.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.09</td>\n",
       "      <td>7.9</td>\n",
       "      <td>68.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.09</td>\n",
       "      <td>6.9</td>\n",
       "      <td>57.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.09</td>\n",
       "      <td>6.7</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.09</td>\n",
       "      <td>6.7</td>\n",
       "      <td>39.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      IR   UER  TEMP\n",
       "0   1.41   4.0  31.7\n",
       "1   1.42   4.1  42.0\n",
       "2   1.51   4.0  40.1\n",
       "3   1.69   4.0  49.5\n",
       "4   1.70   3.8  66.9\n",
       "5   1.82   4.0  71.7\n",
       "6   1.91   3.8  77.6\n",
       "7   1.91   3.8  78.1\n",
       "8   1.95   3.7  70.7\n",
       "9   2.19   3.8  57.7\n",
       "10  2.20   3.8  44.4\n",
       "11  2.27   3.9  40.1\n",
       "12  2.40   4.0  32.5\n",
       "13  2.40   3.8  36.2\n",
       "14  2.41   3.8  41.7\n",
       "15  2.42   3.6  55.5\n",
       "16  2.39   3.7  62.2\n",
       "17  2.38   3.6  71.7\n",
       "18  2.40   3.7  79.6\n",
       "19  2.13   3.7  75.5\n",
       "20  2.04   3.5  70.4\n",
       "21  1.83   3.6  59.9\n",
       "22  1.55   3.6  43.9\n",
       "23  1.55   3.6  38.3\n",
       "24  1.55   3.5  39.1\n",
       "25  1.58   3.5  40.1\n",
       "26  0.65   4.4  48.0\n",
       "27  0.05  14.7  50.4\n",
       "28  0.05  13.2  60.3\n",
       "29  0.08  11.0  73.7\n",
       "30  0.09  10.2  80.0\n",
       "31  0.10   8.4  76.9\n",
       "32  0.09   7.9  68.8\n",
       "33  0.09   6.9  57.9\n",
       "34  0.09   6.7  53.0\n",
       "35  0.09   6.7  39.2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Case: ['IR']\n",
      "Intercept: 3277.140101059066\n",
      "Coefficients: [-212.12280279]\n",
      "Case: ['UER']\n",
      "Intercept: 2707.1160953691406\n",
      "Coefficients: [50.04122642]\n",
      "Case: ['TEMP']\n",
      "Intercept: 2625.204874501264\n",
      "Coefficients: [6.10109836]\n",
      "Case: ['IR', 'UER']\n",
      "Intercept: 3337.3311158896545\n",
      "Coefficients: [-229.59372733   -6.65731275]\n",
      "Case: ['IR', 'UER']\n",
      "Intercept: 3337.3311158896545\n",
      "Coefficients: [-229.59372733   -6.65731275]\n",
      "Case: ['UER', 'TEMP']\n",
      "Intercept: 2486.1616299002903\n",
      "Coefficients: [45.19539068  4.37731589]\n",
      "Case: ['IR', 'UER', 'TEMP']\n",
      "Intercept: 3115.3068323919247\n",
      "Coefficients: [-244.9313694   -16.23758849    5.23256066]\n",
      "Test Case: {'IR': 2.75, 'UER': 5.3, 'TEMP': 38.3}\n",
      "Case 1 Prediction: 2670.6646081473427\n",
      "Case 2 Prediction: 2618.191157081313\n",
      "\n",
      "Test Case: {'IR': 2.9, 'UER': 5.9, 'TEMP': 57.7}\n",
      "Case 1 Prediction: 2632.2311613972297\n",
      "Case 2 Prediction: 2682.1791300851896\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Step 4:  Fit the regression model for ALL combinations of indep vars\n",
    "#         Call the function you created in Step 3.\n",
    "#         Print out the intercept, coefficients and r2 score for each of the cases.\n",
    "#         Hint:  Extract the X parameter from the dataframe.\n",
    "#         Hint:  The Y parameter will be Stock_Index_Price for all cases.\n",
    "#Case 1:  Interest Rate only\n",
    "#Case 2:  Unemployment Rate only\n",
    "#Case 3:  GDP only\n",
    "#Case 4:  Interest Rate + Unemployment Rate\n",
    "#Case 5:  Interest Rate + GDP\n",
    "#Case 6:  Unemployment Rate + GDP\n",
    "#Case 7:  Interest Rate + Unemployment Rate + GDP\n",
    "# YOUR CODE HERE...\n",
    "\n",
    "\n",
    "\n",
    "cases = [\n",
    "    ['IR'],\n",
    "    ['UER'],\n",
    "    ['TEMP'],\n",
    "    ['IR', 'UER'],\n",
    "    ['IR', 'UER'],\n",
    "    ['UER', 'TEMP'],\n",
    "    ['IR', 'UER', 'TEMP']\n",
    "]\n",
    "\n",
    "for case in cases:\n",
    "    X_case = df[case]\n",
    "    intercept, coefficients, score = MLR(X_case, Y)\n",
    "    print('Case:', case)\n",
    "    print('Intercept:', intercept)\n",
    "    print('Coefficients:', coefficients)\n",
    "    \n",
    "\n",
    "#Step 5:  Predict the stock index price using your top 2 models with the following test cases (i.e. the top 2 models with the best r2 scores):\n",
    "#         You should regenerate your model in case it was overwritten in the previous step when testing the other conditions.\n",
    "#         Set the values for each of the independent variables, then call the sklearn predict function.\n",
    "#         Hint:  https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression.predict\n",
    "#         Test case 1:  Predict the stock index price based on the following:\n",
    "#                       New_Interest_Rate = 2.75\n",
    "#                       New_Unemployment_Rate = 5.3\n",
    "#                       New_GDP = 20.0\n",
    "#         Test case 2:  Predict the stock index price based on the following:\n",
    "#                       New_Interest_Rate = 2.9\n",
    "#                       New_Unemployment_Rate = 5.9\n",
    "#                       New_GDP = 21.0\n",
    "# NOTE - **** if your model does not use all 3 of the parameters given in the test cases above, then just leave them out **** \n",
    "# YOUR CODE HERE...\n",
    "\n",
    "test_cases = [\n",
    "    {'IR': 2.75, 'UER': 5.3, 'TEMP': 38.3},\n",
    "    {'IR': 2.9, 'UER': 5.9, 'TEMP': 57.7}\n",
    "]\n",
    "\n",
    "# Generate the models for the top 2 cases\n",
    "X_case1 = df[['IR', 'UER']]\n",
    "X_case2 = df[['IR', 'TEMP']]\n",
    "Y = df['stock_index_price']\n",
    "intercept1, coefficients1, _ = MLR(X_case1, Y)\n",
    "intercept2, coefficients2, _ = MLR(X_case2, Y)\n",
    "\n",
    "# Predict the stock index price for the test cases\n",
    "for test_case in test_cases:\n",
    "    case1_prediction = intercept1 + coefficients1[0] * test_case['IR'] + coefficients1[1] * test_case['UER']\n",
    "    case2_prediction = intercept2 + coefficients2[0] * test_case['IR'] + coefficients2[1] * test_case['TEMP']\n",
    "    print('Test Case:', test_case)\n",
    "    print('Case 1 Prediction:', case1_prediction)\n",
    "    print('Case 2 Prediction:', case2_prediction)\n",
    "    print()\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "uucNID33jORm",
    "Gpl29vJWzHPJ",
    "f4rBpbqCItcC",
    "EvK2ywSxBmuW"
   ],
   "provenance": []
  },
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}