QM-7063-Learning-Practice-5/Schrick-Noah_Learning-Practice-5.ipynb

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    "# Learning Practice 5 for the University of Tulsa's QM-7063 Data Mining Course\n",
    "# Prediction and Classification Methods\n",
    "# # Professor: Dr. Abdulrashid, Spring 2023\n",
    "# Noah L. Schrick - 1492657\n",
    "\n",
    "%matplotlib inline\n",
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge, LassoCV, BayesianRidge\n",
    "import statsmodels.formula.api as sm\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "from dmba import regressionSummary, exhaustive_search\n",
    "from dmba import backward_elimination, forward_selection, stepwise_selection\n",
    "from dmba import adjusted_r2_score, AIC_score, BIC_score\n"
   ]
  },
  {
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   "source": [
    "# Problem 6.1\n",
    "The file BostonHousing.csv contains information collected by the US Bureau of the Census concerning housing in the area of Boston, Massachusetts. The dataset includes information on 506 census housing tracts in the Boston area. The goal is to predict the median house price in new tracts based on information such as crime rate, pollution, and number of rooms. The dataset contains 13 predictors, and the outcome variable is the median house price (MEDV). Table 6.11 describes each of the predictors and the outcome variable.\n",
    "\n",
    "TABLE 6.11 DESCRIPTION OF VARIABLES FOR BOSTON HOUSING EXAMPLE\n",
    "CRIM    Per capita crime rate by town\n",
    "ZN      Proportion of residential land zoned for lots over 25,000 ft2\n",
    "INDUS   Proportion of nonretail business acres per town\n",
    "CHAS    Charles River dummy variable (=1 if tract bounds river; =0 otherwise)\n",
    "NOX     Nitric oxide concentration (parts per 10 million)\n",
    "RM      Average number of rooms per dwelling\n",
    "AGE     Proportion of owner-occupied units built prior to 1940\n",
    "DIS     Weighted distances to five Boston employment centers\n",
    "RAD     Index of accessibility to radial highways\n",
    "TAX     Full-value property-tax rate per $10,000\n",
    "PTRATIO Pupil/teacher ratio by town\n",
    "LSTAT   Percentage lower status of the population\n",
    "MEDV    Median value of owner-occupied homes in $100\n",
    "\n",
    "\n",
    "a.\n",
    "    Why should the data be partitioned into training and validation sets? What will the training set be used for? What will the validation set be used for?\n",
    "b.\n",
    "    Fit a multiple linear regression model to the median house price (MEDV) as a function of CRIM, CHAS, and RM. Write the equation for predicting the median house price from the predictors in the model.\n",
    "c. \n",
    "    Using the estimated regression model, what median house price is predicted for a tract in the Boston area that does not bound the Charles River, has a crime rate of 0.1, and where the average number of rooms per house is 6?\n",
    "d. \n",
    "    Reduce the number of predictors:\n",
    "        i. Which predictors are likely to be measuring the same thing among the 13 predictors? Discuss the relationships among INDUS, NOX, and TAX.\n",
    "        ii. Compute the correlation table for the 12 numerical predictors and search for highly correlated pairs. These have potential redundancy and can cause multi-collinearity. Choose which ones to remove based on this table.\n",
    "        iii. Use three subset selection algorithms: backward, forward, and stepwise) to reduce the remaining predictors. Compute the validation performance for each of the three selected models. Compare RMSE, MAPE, and mean error, as well as histograms of the errors. Finally, describe the best model."
   ]
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   "source": [
    "# a.\n",
    "The training set is used to train and build the model. The data within this set is fed directly into the model to provide a fit.\n",
    "\n",
    "The validation set is used to evaluate and rate the model. This data is unknown to the model since it is not fed into the training, and is not used in any other way. The model attempts to classify this unknown data, and its performance and accuracy is checked.\n",
    "\n",
    "Partitioning the data allows us to confirm if the model is working as intended. Having a set of data used to measure the model lets us check if the fit is acceptable or not. If we were to use all of the data for training the model, and then use that same data to check performance and accuracy, we would have biased results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intercept  -29.193467430606834\n",
      "  Predictor  coefficient\n",
      "0      CRIM    -0.240062\n",
      "1      CHAS     3.266817\n",
      "2        RM     8.325175\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : -0.0000\n",
      "       Root Mean Squared Error (RMSE) : 5.9666\n",
      "            Mean Absolute Error (MAE) : 3.9668\n",
      "          Mean Percentage Error (MPE) : -7.2747\n",
      "Mean Absolute Percentage Error (MAPE) : 22.5927\n"
     ]
    }
   ],
   "source": [
    "# b.\n",
    "housing_df = pd.read_csv('BostonHousing.csv')\n",
    "housing_df = housing_df.drop('CAT. MEDV', axis=1)\n",
    "#housing_b_df = housing_df[['MEDV', 'CRIM', 'CHAS', 'RM']]\n",
    "predictors = ['CRIM', 'CHAS', 'RM']\n",
    "outcome = 'MEDV'\n",
    "\n",
    "# partition data\n",
    "X = pd.get_dummies(housing_df[predictors], drop_first=True)\n",
    "y = housing_df[outcome]\n",
    "train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size=0.4, random_state=1)\n",
    "housing_lm = LinearRegression()\n",
    "housing_lm.fit(train_X, train_y)\n",
    "\n",
    "# print coefficients\n",
    "print('intercept ', housing_lm.intercept_)\n",
    "print(pd.DataFrame({'Predictor': X.columns, 'coefficient': housing_lm.coef_}))\n",
    "\n",
    "# print performance measures\n",
    "regressionSummary(train_y, housing_lm.predict(train_X))\n",
    "\n",
    "# Equation:\n",
    "# MEDV = -29.19 -0.24*CRIM + 3.27*CHAS + 8.33*RM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21.08426608]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/noah/.local/lib/python3.10/site-packages/sklearn/base.py:420: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# c.\n",
    "housing_lm_pred = housing_lm.predict([[0, 0.1, 6]])\n",
    "print(housing_lm_pred)\n",
    "\n",
    "# Since no other samples exist for this data point, no residual error is able to be obtained."
   ]
  },
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   "execution_count": 25,
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     "execution_count": 25,
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",
      "text/plain": [
       "<Figure size 1100x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# d.\n",
    "# i.\n",
    "corr = housing_df.corr()\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11, 7)\n",
    "sns.heatmap(corr, annot=True, fmt=\".1f\", cmap=\"RdBu\", center=0, ax=ax)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relationship between INDUS, RAD, and TAX\n",
    "RAD and TAX are strongly correlated, measuring at 0.9.\n",
    "INDUS and RAD are positively correlated, measuring at 0.6.\n",
    "INDUS and TAX is a bit more positively correlated, measuring at 0.7.\n",
    "All three of these indicate that they would be measuring the same thing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# d.\n",
    "# ii.\n",
    "sns.heatmap(corr, annot=True, fmt=\".1f\", cmap=\"RdBu\", center=0, ax=ax)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Highly Correlated Pairs\n",
    "ZN and DIS\n",
    "RAD and TAX\n",
    "PTRATIO and RAD\n",
    "PTRATIO and TAX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backward\n",
      "Variables: CRIM, CHAS, RM\n",
      "Start: score=1952.30\n",
      "Step: score=1952.30, remove None\n",
      "['CRIM', 'CHAS', 'RM']\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : -0.0000\n",
      "       Root Mean Squared Error (RMSE) : 5.9666\n",
      "            Mean Absolute Error (MAE) : 3.9668\n",
      "          Mean Percentage Error (MPE) : -7.2747\n",
      "Mean Absolute Percentage Error (MAPE) : 22.5927\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# d.\n",
    "# iii.\n",
    "def train_model(variables):\n",
    "    if len(variables) == 0:\n",
    "        return None\n",
    "    model = LinearRegression()\n",
    "    model.fit(train_X[variables], train_y)\n",
    "    return model\n",
    "\n",
    "def score_model(model, variables):\n",
    "    if len(variables) == 0:\n",
    "        return AIC_score(train_y, [train_y.mean()] * len(train_y), model, df=1)\n",
    "    return AIC_score(train_y, model.predict(train_X[variables]), model)\n",
    "\n",
    "print(\"Backward\")\n",
    "best_back_model, best_back_variables = backward_elimination(train_X.columns, train_model, score_model, verbose=True)\n",
    "print(best_back_variables)\n",
    "regressionSummary(train_y, best_back_model.predict(train_X))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forward\n",
      "Variables: CRIM, CHAS, RM\n",
      "Start: score=2191.75, constant\n",
      "Step: score=1989.28, add RM\n",
      "Step: score=1956.79, add CRIM\n",
      "Step: score=1952.30, add CHAS\n",
      "Step: score=1952.30, add None\n",
      "['RM', 'CRIM', 'CHAS']\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : -0.0000\n",
      "       Root Mean Squared Error (RMSE) : 5.9666\n",
      "            Mean Absolute Error (MAE) : 3.9668\n",
      "          Mean Percentage Error (MPE) : -7.2747\n",
      "Mean Absolute Percentage Error (MAPE) : 22.5927\n"
     ]
    }
   ],
   "source": [
    "print(\"Forward\")\n",
    "best_forw_model, best_forw_variables = forward_selection(train_X.columns, train_model, score_model, verbose=True)\n",
    "print(best_forw_variables)\n",
    "forw_train_X = train_X.loc[:,['RM','CRIM','CHAS']]\n",
    "regressionSummary(train_y, best_forw_model.predict(forw_train_X))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stepwise\n",
      "Variables: CRIM, CHAS, RM\n",
      "Start: score=2191.75, constant\n",
      "Step: score=1989.28, add RM\n",
      "Step: score=1956.79, add CRIM\n",
      "Step: score=1952.30, add CHAS\n",
      "Step: score=1952.30, add None\n",
      "['RM', 'CRIM', 'CHAS']\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : -0.0000\n",
      "       Root Mean Squared Error (RMSE) : 5.9666\n",
      "            Mean Absolute Error (MAE) : 3.9668\n",
      "          Mean Percentage Error (MPE) : -7.2747\n",
      "Mean Absolute Percentage Error (MAPE) : 22.5927\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : -0.0000\n",
      "       Root Mean Squared Error (RMSE) : 5.9666\n",
      "            Mean Absolute Error (MAE) : 3.9668\n",
      "          Mean Percentage Error (MPE) : -7.2747\n",
      "Mean Absolute Percentage Error (MAPE) : 22.5927\n"
     ]
    }
   ],
   "source": [
    "# d iii. continued\n",
    "print(\"Stepwise\")\n",
    "best_step_model, best_step_variables = forward_selection(train_X.columns, train_model, score_model, verbose=True)\n",
    "print(best_step_variables)\n",
    "step_train_X = train_X.loc[:,['RM','CRIM','CHAS']]\n",
    "regressionSummary(train_y, best_step_model.predict(step_train_X))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LASSO\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : 0.2627\n",
      "       Root Mean Squared Error (RMSE) : 6.7153\n",
      "            Mean Absolute Error (MAE) : 4.7355\n",
      "          Mean Percentage Error (MPE) : -8.5983\n",
      "Mean Absolute Percentage Error (MAPE) : 23.9824\n",
      "\n",
      "\n",
      "LASSO CV\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : 0.1124\n",
      "       Root Mean Squared Error (RMSE) : 6.4186\n",
      "            Mean Absolute Error (MAE) : 4.4592\n",
      "          Mean Percentage Error (MPE) : -7.7091\n",
      "Mean Absolute Percentage Error (MAPE) : 23.1854\n",
      "Lasso-CV chosen regularization:  0.033515828458353755\n",
      "[-0.24201538  2.81692528  8.25934245]\n",
      "\n",
      "\n",
      "RIDGE\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : 0.1201\n",
      "       Root Mean Squared Error (RMSE) : 6.4138\n",
      "            Mean Absolute Error (MAE) : 4.4590\n",
      "          Mean Percentage Error (MPE) : -7.6484\n",
      "Mean Absolute Percentage Error (MAPE) : 23.1724\n",
      "\n",
      "\n",
      "BAYESIAN RIDGE\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : 0.1211\n",
      "       Root Mean Squared Error (RMSE) : 6.4144\n",
      "            Mean Absolute Error (MAE) : 4.4603\n",
      "          Mean Percentage Error (MPE) : -7.6595\n",
      "Mean Absolute Percentage Error (MAPE) : 23.1747\n",
      "Bayesian ridge chosen regularization:  1.3591395967339095\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# d iii model\n",
    "print(\"LASSO\")\n",
    "lasso = Lasso(alpha=1)\n",
    "lasso.fit(train_X, train_y)\n",
    "regressionSummary(valid_y, lasso.predict(valid_X))\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"LASSO CV\")\n",
    "lasso_cv = LassoCV(cv=5)\n",
    "lasso_cv.fit(train_X, train_y)\n",
    "regressionSummary(valid_y, lasso_cv.predict(valid_X))\n",
    "print('Lasso-CV chosen regularization: ', lasso_cv.alpha_)\n",
    "print(lasso_cv.coef_)\n",
    "print(\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RIDGE\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : 0.1201\n",
      "       Root Mean Squared Error (RMSE) : 6.4138\n",
      "            Mean Absolute Error (MAE) : 4.4590\n",
      "          Mean Percentage Error (MPE) : -7.6484\n",
      "Mean Absolute Percentage Error (MAPE) : 23.1724\n",
      "\n",
      "\n",
      "BAYESIAN RIDGE\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "                      Mean Error (ME) : 0.1211\n",
      "       Root Mean Squared Error (RMSE) : 6.4144\n",
      "            Mean Absolute Error (MAE) : 4.4603\n",
      "          Mean Percentage Error (MPE) : -7.6595\n",
      "Mean Absolute Percentage Error (MAPE) : 23.1747\n",
      "Bayesian ridge chosen regularization:  1.3591395967339095\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"RIDGE\")\n",
    "ridge = Ridge(alpha=1)\n",
    "ridge.fit(train_X, train_y)\n",
    "regressionSummary(valid_y, ridge.predict(valid_X))\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"BAYESIAN RIDGE\")\n",
    "bayesianRidge = BayesianRidge()\n",
    "bayesianRidge.fit(train_X, train_y)\n",
    "regressionSummary(valid_y, bayesianRidge.predict(valid_X))\n",
    "print('Bayesian ridge chosen regularization: ', bayesianRidge.lambda_ / bayesianRidge.alpha_)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Best model\n",
    "Bayesian Ridge: Lowest MAPE\n",
    "Ridge: Lowest RMSE, lowest MAE\n",
    "\n",
    "Ridge or Bayesian Ridge should be used. Further parameter tuning can assist in selection which of the two models to use."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 6.2\n",
    "Tayko Software is a software catalog firm that sells games and educational software. It started out as a software manufacturer and then added third-party titles to its offerings. It recently revised its collection of items in a new catalog, which it mailed out to its customers. This mailing yielded 2000 purchases. Based on these data, Tayko wants to devise a model for predicting the spending amount that a purchasing customer will yield. The file Tayko.csv contains information on 2000 purchases. Table 6.12 describes the variables to be used in the problem (the Excel file contains additional variables).\n",
    "\n",
    "TABLE 6.12 DESCRIPTION OF VARIABLES FOR TAYKO SOFTWARE EXAMPLE\n",
    "FREQ                Number of transactions in the preceding year\n",
    "LAST_UPDATE         Number of days since last update to customer record\n",
    "WEB                 Whether customer purchased by Web order at least once\n",
    "GENDER              Male or female\n",
    "ADDRESS_RES         Whether it is a residential address\n",
    "ADDRESS_US          Whether it is a US address\n",
    "SPENDING (outcome)  Amount spent by customer in test mailing ($)\n",
    "\n",
    "a. \n",
    "    Explore the spending amount by creating a pivot table for the categorical variables and computing the average and standard deviation of spending in each category.\n",
    "b. \n",
    "    Explore the relationship between spending and each of the two continuous predictors by creating two scatterplots (Spending vs. Freq, and Spending vs. last_update_days_ago). Does there seem to be a linear relationship?\n",
    "c. \n",
    "    To fit a predictive model for Spending:\n",
    "    i. Partition the 2000 records into training and validation sets.\n",
    "    ii. Run a multiple linear regression model for Spending vs. all six predictors. Give the estimated predictive equation.\n",
    "    iii. Based on this model, what type of purchaser is most likely to spend a large amount of money?\n",
    "    iv. If we used backward elimination to reduce the number of predictors, which predictor would be dropped first from the model?\n",
    "    v. Show how the prediction and the prediction error are computed for the first purchase in the validation set.\n",
    "    vi. Evaluate the predictive accuracy of the model by examining its performance on the validation set.\n",
    "    vii. Create a histogram of the model residuals. Do they appear to follow a normal distribution? How does this affect the predictive performance of the model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   mean                                            std  \\\n",
      "         Address_is_res Gender=male        US Web order Address_is_res   \n",
      "Spending                                                                 \n",
      "0              0.219219    0.535536  0.815816  0.307307       0.413925   \n",
      "1              0.000000    0.000000  1.000000  1.000000            NaN   \n",
      "3              0.666667    0.333333  0.666667  0.333333       0.577350   \n",
      "4              0.500000    0.000000  1.000000  0.500000       0.707107   \n",
      "6              0.666667    0.666667  1.000000  0.666667       0.577350   \n",
      "...                 ...         ...       ...       ...            ...   \n",
      "1416           0.000000    1.000000  1.000000  0.000000            NaN   \n",
      "1441           0.000000    1.000000  0.000000  0.000000            NaN   \n",
      "1443           0.000000    0.000000  1.000000  1.000000            NaN   \n",
      "1446           0.000000    1.000000  1.000000  0.000000            NaN   \n",
      "1500           0.000000    1.000000  1.000000  0.000000            NaN   \n",
      "\n",
      "                                          \n",
      "         Gender=male        US Web order  \n",
      "Spending                                  \n",
      "0           0.498985  0.387828  0.461609  \n",
      "1                NaN       NaN       NaN  \n",
      "3           0.577350  0.577350  0.577350  \n",
      "4           0.000000  0.000000  0.707107  \n",
      "6           0.577350  0.000000  0.577350  \n",
      "...              ...       ...       ...  \n",
      "1416             NaN       NaN       NaN  \n",
      "1441             NaN       NaN       NaN  \n",
      "1443             NaN       NaN       NaN  \n",
      "1446             NaN       NaN       NaN  \n",
      "1500             NaN       NaN       NaN  \n",
      "\n",
      "[363 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "# a\n",
    "tayko_df = pd.read_csv('Tayko.csv')\n",
    "table = pd.pivot_table(tayko_df, values = ['Web order', 'Gender=male', 'Address_is_res', 'US'],\n",
    "                       index='Spending',\n",
    "                       aggfunc=['mean', 'std'])\n",
    "\n",
    "print(table)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Spending', ylabel='last_update_days_ago'>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# b. \n",
    "## Scatter plot with pandas\n",
    "tayko_df.plot.scatter(x='Spending', y='Freq', legend=False)\n",
    "tayko_df.plot.scatter(x='Spending', y='last_update_days_ago', legend=False)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear? \n",
    "There does not appear to be a linear relationship between spending and last update days ago.\n",
    "An argument could be made for Frequency and Spending as spending gets larger, but both scatter plots do not seem to indicate a linear relationship. The linear fit for frequency and spending would have a low R squared value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# c. i\n",
    "predictors = ['US','Freq', 'last_update_days_ago', 'Web order', 'Gender=male', 'Address_is_res']\n",
    "\n",
    "outcome = 'Spending'\n",
    "X = pd.get_dummies(tayko_df[predictors], drop_first=True)\n",
    "y = tayko_df[outcome]\n",
    "train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size=0.4, random_state=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intercept  10.17629741458822\n",
      "              Predictor  coefficient\n",
      "0                    US    -4.620293\n",
      "1                  Freq    91.274450\n",
      "2  last_update_days_ago    -0.010374\n",
      "3             Web order    18.628731\n",
      "4           Gender=male    -9.111366\n",
      "5        Address_is_res   -75.815354\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "               Mean Error (ME) : 7.1933\n",
      "Root Mean Squared Error (RMSE) : 136.7397\n",
      "     Mean Absolute Error (MAE) : 83.6010\n"
     ]
    }
   ],
   "source": [
    "# c. ii\n",
    "tayko_lm = LinearRegression()\n",
    "tayko_lm.fit(train_X, train_y)\n",
    "\n",
    "# print coefficients\n",
    "print('intercept ', tayko_lm.intercept_)\n",
    "print(pd.DataFrame({'Predictor': X.columns, 'coefficient': tayko_lm.coef_}))\n",
    "\n",
    "# print performance measures\n",
    "regressionSummary(valid_y, tayko_lm.predict(valid_X))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# iii Based on this model, what type of purchaser is most likely to spend a large amount of money?\n",
    "Women outside the US that do not have a residential address, that place web orders, and made many transactions the previous year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backward\n",
      "Variables: US, Freq, last_update_days_ago, Web order, Gender=male, Address_is_res\n",
      "Start: score=15028.53\n",
      "Step: score=15026.76, remove US\n",
      "Step: score=15026.38, remove Gender=male\n",
      "Step: score=15026.38, remove None\n",
      "['Freq', 'last_update_days_ago', 'Web order', 'Address_is_res']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#iv. If we used backward elimination to reduce the number\n",
    "# of predictors, which predictor would be dropped first \n",
    "# from the model?\n",
    "\n",
    "def train_model(variables):\n",
    "    if len(variables) == 0:\n",
    "        return None\n",
    "    model = LinearRegression()\n",
    "    model.fit(train_X[variables], train_y)\n",
    "    return model\n",
    "\n",
    "def score_model(model, variables):\n",
    "    if len(variables) == 0:\n",
    "        return AIC_score(train_y, [train_y.mean()] * len(train_y), model, df=1)\n",
    "    return AIC_score(train_y, model.predict(train_X[variables]), model)\n",
    "\n",
    "print(\"Backward\")\n",
    "best_back_model, best_back_variables = backward_elimination(train_X.columns, train_model, score_model, verbose=True)\n",
    "print(best_back_variables)\n",
    "\n",
    "# 'US' dropped first"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# v. Show how the prediction and the prediction error are computed for the first purchase in the validation set.\n",
    "\n",
    "After the model is trained, we have the regression coefficients.\n",
    "Using these, we can multiply them with the new predictor values.\n",
    "Using the sample of the first purchase, each predictor is multiplied by the coefficients to compute the prediction.\n",
    "\n",
    "The error is obtained by comparing the predicted value to the actual value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Predicted  Actual     Residual\n",
      "674    89.214915       0   -89.214915\n",
      "1699  202.231362     184   -18.231362\n",
      "1282   49.159303       0   -49.159303\n",
      "1315  824.841659    1289   464.158341\n",
      "1210    0.121196       0    -0.121196\n",
      "1636   86.766675       0   -86.766675\n",
      "613    58.018614       0   -58.018614\n",
      "447   247.428569    1255  1007.571431\n",
      "1131   67.036615       0   -67.036615\n",
      "808    67.825031       0   -67.825031\n",
      "1496   -7.098168       0     7.098168\n",
      "1468  194.814024     411   216.185976\n",
      "1682  -13.480101       0    13.480101\n",
      "1149  -32.457046       0    32.457046\n",
      "442    61.247979       0   -61.247979\n",
      "1813    4.497885     173   168.502115\n",
      "654   -46.046854       0    46.046854\n",
      "1264  -32.315195       0    32.315195\n",
      "858    80.219048       0   -80.219048\n",
      "1482   51.783900       0   -51.783900\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "               Mean Error (ME) : 7.1933\n",
      "Root Mean Squared Error (RMSE) : 136.7397\n",
      "     Mean Absolute Error (MAE) : 83.6010\n"
     ]
    }
   ],
   "source": [
    "#vi. Evaluate the predictive accuracy of the model by\n",
    "# examining its performance on the validation set.\n",
    "\n",
    "tayko_lm_pred = tayko_lm.predict(valid_X)\n",
    "\n",
    "result = pd.DataFrame({'Predicted': tayko_lm_pred, 'Actual': valid_y,\n",
    "                       'Residual': valid_y - tayko_lm_pred})\n",
    "print(result.head(20))\n",
    "\n",
    "# Compute common accuracy measures\n",
    "regressionSummary(valid_y, tayko_lm_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#vii. Create a histogram of the model residuals. \n",
    "# Do they appear to follow a normal distribution? \n",
    "# How does this affect the predictive performance of the model?\n",
    "\n",
    "tayko_lm_pred = tayko_lm.predict(valid_X)\n",
    "all_residuals = valid_y - tayko_lm_pred\n",
    "\n",
    "ax = pd.DataFrame({'Residuals': all_residuals}).hist(bins=25)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
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  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
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 },
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}