2-linear-models-ordinal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Diamonds Price Prediction - Ordinal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In this notebook we will follow with the implemenation of linear models in the diamonds dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/diego/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "#import general libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40339, 9)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.21</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>VS2</td>\n",
       "      <td>63.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>6134</td>\n",
       "      <td>190.734930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>VVS2</td>\n",
       "      <td>64.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>532</td>\n",
       "      <td>45.921708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.42</td>\n",
       "      <td>Premium</td>\n",
       "      <td>F</td>\n",
       "      <td>VS1</td>\n",
       "      <td>61.2</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1103</td>\n",
       "      <td>69.338592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.26</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>IF</td>\n",
       "      <td>61.1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>600</td>\n",
       "      <td>43.362176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.10</td>\n",
       "      <td>Good</td>\n",
       "      <td>G</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.4</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4997</td>\n",
       "      <td>176.802840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  carat        cut color clarity  depth  table  price      volume\n",
       "0           0   1.21      Ideal     H     VS2   63.0   57.0   6134  190.734930\n",
       "1           1   0.28  Very Good     D    VVS2   64.0   56.0    532   45.921708\n",
       "2           2   0.42    Premium     F     VS1   61.2   58.0   1103   69.338592\n",
       "3           3   0.26      Ideal     H      IF   61.1   57.0    600   43.362176\n",
       "4           4   1.10       Good     G     SI1   63.4   57.0   4997  176.802840"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindf = pd.read_csv(\"OUTPUT/cleaned_train.csv\")\n",
    "print(traindf.shape)\n",
    "traindf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To implement linear models in our data we need int instead of categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ordinal Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ord = traindf.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cut_dict = {\n",
    "    \"Fair\":1, \n",
    "    \"Good\":2, \n",
    "    \"Very Good\":3, \n",
    "    \"Premium\":4, \n",
    "    \"Ideal\":5\n",
    "}\n",
    "\n",
    "color_dict = {\n",
    "    \"J\":1,\n",
    "    \"I\":2,\n",
    "    \"H\":3, \n",
    "    \"G\":4, \n",
    "    \"F\":5, \n",
    "    \"E\":6, \n",
    "    \"D\":7\n",
    "}\n",
    "\n",
    "clarity_dict = {\n",
    "    \"I1\":1, \n",
    "    \"SI2\":2, \n",
    "    \"SI1\":3, \n",
    "    \"VS2\":4, \n",
    "    \"VS1\":5,\n",
    "    \"VVS2\":6,\n",
    "    \"VVS1\":7,\n",
    "    \"IF\":8\n",
    "}\n",
    "\n",
    "train_ord.cut = train_ord.cut.map(cut_dict)\n",
    "train_ord.color = train_ord.color.map(color_dict)\n",
    "train_ord.clarity = train_ord.clarity.map(clarity_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.21</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>63.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>6134</td>\n",
       "      <td>190.734930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.28</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>64.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>532</td>\n",
       "      <td>45.921708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.42</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>61.2</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1103</td>\n",
       "      <td>69.338592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.26</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>61.1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>600</td>\n",
       "      <td>43.362176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.10</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>63.4</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4997</td>\n",
       "      <td>176.802840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat  cut  color  clarity  depth  table  price      volume\n",
       "0   1.21    5      3        4   63.0   57.0   6134  190.734930\n",
       "1   0.28    3      7        6   64.0   56.0    532   45.921708\n",
       "2   0.42    4      5        5   61.2   58.0   1103   69.338592\n",
       "3   0.26    5      3        8   61.1   57.0    600   43.362176\n",
       "4   1.10    2      4        3   63.4   57.0   4997  176.802840"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ord.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### But before implementing the linear models, we have to check 3 steps:\n",
    "    - Correlation between variables\n",
    "    - How each independent variable explain price\n",
    "    - If we need to scale our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation matrix')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,9))\n",
    "\n",
    "sns.heatmap(train_ord.corr(), annot = True, fmt='.1g', vmin=-1, vmax=1, center= 0, cmap= 'coolwarm' , linewidths=3, linecolor='black')\n",
    "\n",
    "plt.title('Correlation matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we have seen in the descriptive analysis, the price is highly correlated with carat and volume and also these to independet variables too. The problem with the corr between the 2 independent variables is that it causes the predictive coefficients to be unstable, because the \"independent variables\" have become \"dependent\". So we are going to do an OLS regression using statsmodels, to check the effect of the independent variables on the dependent one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_t = train_ord.drop(columns={\"price\"})\n",
    "y_t = train_ord.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1222.959824435647\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th>  <td>   0.906</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.906</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>5.529e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 10 May 2020</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>18:06:13</td>     <th>  Log-Likelihood:    </th> <td>-3.4401e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 40339</td>      <th>  AIC:               </th>  <td>6.880e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 40331</td>      <th>  BIC:               </th>  <td>6.881e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>   <td>-3171.9144</td> <td>  444.239</td> <td>   -7.140</td> <td> 0.000</td> <td>-4042.633</td> <td>-2301.196</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>carat</th>   <td> 8687.8685</td> <td>   54.754</td> <td>  158.670</td> <td> 0.000</td> <td> 8580.549</td> <td> 8795.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut</th>     <td>  117.6842</td> <td>    6.641</td> <td>   17.720</td> <td> 0.000</td> <td>  104.667</td> <td>  130.701</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>color</th>   <td>  318.5792</td> <td>    3.791</td> <td>   84.042</td> <td> 0.000</td> <td>  311.149</td> <td>  326.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>clarity</th> <td>  524.7211</td> <td>    4.049</td> <td>  129.583</td> <td> 0.000</td> <td>  516.784</td> <td>  532.658</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>depth</th>   <td>  -40.9819</td> <td>    4.912</td> <td>   -8.344</td> <td> 0.000</td> <td>  -50.609</td> <td>  -31.355</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>table</th>   <td>  -24.3823</td> <td>    3.418</td> <td>   -7.134</td> <td> 0.000</td> <td>  -31.081</td> <td>  -17.683</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>volume</th>  <td>    0.9283</td> <td>    0.326</td> <td>    2.851</td> <td> 0.004</td> <td>    0.290</td> <td>    1.566</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>7986.505</td> <th>  Durbin-Watson:     </th>  <td>   2.004</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>126048.248</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.512</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>11.599</td>  <th>  Cond. No.          </th>  <td>1.23e+04</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.23e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.906\n",
       "Model:                            OLS   Adj. R-squared:                  0.906\n",
       "Method:                 Least Squares   F-statistic:                 5.529e+04\n",
       "Date:                Sun, 10 May 2020   Prob (F-statistic):               0.00\n",
       "Time:                        18:06:13   Log-Likelihood:            -3.4401e+05\n",
       "No. Observations:               40339   AIC:                         6.880e+05\n",
       "Df Residuals:                   40331   BIC:                         6.881e+05\n",
       "Df Model:                           7                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const      -3171.9144    444.239     -7.140      0.000   -4042.633   -2301.196\n",
       "carat       8687.8685     54.754    158.670      0.000    8580.549    8795.188\n",
       "cut          117.6842      6.641     17.720      0.000     104.667     130.701\n",
       "color        318.5792      3.791     84.042      0.000     311.149     326.009\n",
       "clarity      524.7211      4.049    129.583      0.000     516.784     532.658\n",
       "depth        -40.9819      4.912     -8.344      0.000     -50.609     -31.355\n",
       "table        -24.3823      3.418     -7.134      0.000     -31.081     -17.683\n",
       "volume         0.9283      0.326      2.851      0.004       0.290       1.566\n",
       "==============================================================================\n",
       "Omnibus:                     7986.505   Durbin-Watson:                   2.004\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           126048.248\n",
       "Skew:                           0.512   Prob(JB):                         0.00\n",
       "Kurtosis:                      11.599   Cond. No.                     1.23e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.23e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.regression.linear_model import OLS\n",
    "from statsmodels.tools.eval_measures import rmse\n",
    "\n",
    "X = X_t\n",
    "X = sm.add_constant(X_t)\n",
    "y = y_t\n",
    "model = sm.OLS(y, X).fit()\n",
    "y_pred = model.predict(X)\n",
    "resi = rmse(y, y_pred)\n",
    "print(resi)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As opposite to the dummy version, in the ordinal version all vars seems to be important to predict price, as all p-values are under 1% confidence level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_notdum = train_ord\n",
    "\n",
    "figure = plt.figure(figsize=(15,10))\n",
    "\n",
    "for n, col in enumerate(X_notdum.columns):\n",
    "    ax = figure.add_subplot(3,4,n+1)\n",
    "    ax.set_title(col)\n",
    "    X_notdum[col].hist(ax=ax, bins=50)\n",
    "    \n",
    "figure.tight_layout() #this feature separate the graphs correctly\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see there is a big difference between the values of the explanatory variables so it is better to scale our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "\n",
    "Xss = sc_X.fit_transform(X_t)\n",
    "\n",
    "train_SS = pd.DataFrame(Xss, columns=['carat', 'cut', 'color', 'clarity', 'depth', 'table',\n",
    "       'volume'])\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(15, 5))\n",
    "ax1.set_title('Before Scaling')\n",
    "for e in X_t.columns:\n",
    "    sns.kdeplot(X_t[e], ax=ax1)\n",
    "ax2.set_title('After Standard Scaling')\n",
    "for e in train_SS.columns:\n",
    "    sns.kdeplot(train_SS[e], ax=ax2, legend=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mmc_X = MinMaxScaler()\n",
    "\n",
    "Xmm = mmc_X.fit_transform(X_t)\n",
    "\n",
    "train_MM = pd.DataFrame(Xmm, columns=['carat', 'cut', 'color', 'clarity', 'depth', 'table',\n",
    "       'volume'])\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(15, 5))\n",
    "ax1.set_title('Before Scaling')\n",
    "for e in X_t.columns:\n",
    "    sns.kdeplot(X_t[e], ax=ax1)\n",
    "ax2.set_title('After Min-Max Scaling')\n",
    "for e in train_MM.columns:\n",
    "    sns.kdeplot(train_MM[e], ax=ax2, legend=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### After processing the data, the standard scaler looks better to train our models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32271, 7) (8068, 7) (32271,) (8068,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = train_SS\n",
    "y = np.ravel(y_t) #we use np.ravel to change shape (x,1) to (x,)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1st select the splited X and y. Then after your model achieves the accuracy needed, train with 100% X and y\n",
    "#However if you thing you could be overfitting your data you can use the splited model to test\n",
    "# X, y ----- X_train, y_train\n",
    "X_training = X_train\n",
    "y_training = y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lreg = LinearRegression(fit_intercept=True, normalize=False)\n",
    "\n",
    "lreg.fit(X_training, y_training) \n",
    "  \n",
    "lreg_pred = lreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.897970529749004 rmse: 1291.278461890041\n"
     ]
    }
   ],
   "source": [
    "lreg_r2 = lreg.score(X_test, y_test)\n",
    "\n",
    "lreg_rmse = mean_squared_error(y_test, lreg_pred, sample_weight=None, multioutput='uniform_average', squared=False)\n",
    "\n",
    "print(\"r2:\",lreg_r2,\"rmse:\",lreg_rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check for Heteroskedasticity --> there will be if the residuals don't have a constant variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Residual plot')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(lreg_pred, (lreg_pred - y_test), c='b')\n",
    "\n",
    "plt.hlines(y=0, xmin= -1000, xmax=20000)\n",
    "\n",
    "plt.title('Residual plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see there is Heteroskedasticity as the variance of the residuals get broader when x rises, so we will use a polynomial feature to avoid it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polynomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly_f = PolynomialFeatures(degree = 3) \n",
    "X_poly = poly_f.fit_transform(X_training) \n",
    "  \n",
    "polyreg = LinearRegression()\n",
    "polyreg.fit(X_poly, y_training) \n",
    "\n",
    "poly_pred = polyreg.predict(poly_f.fit_transform(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: -164.59669091586036 rmse: 52021.50572453752\n"
     ]
    }
   ],
   "source": [
    "poly_r2 = polyreg.score(poly_f.fit_transform(X_test), y_test)\n",
    "\n",
    "poly_rmse = mean_squared_error(y_test, poly_pred, sample_weight=None, multioutput='uniform_average', squared=False)\n",
    "\n",
    "print(\"r2:\",poly_r2,\"rmse:\",poly_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Residual plot')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(poly_pred, (poly_pred - y_test), c='b')\n",
    "\n",
    "plt.hlines(y=0, xmin= -1000, xmax=20000)\n",
    "\n",
    "plt.title('Residual plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f68b3f78210>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = X_training.columns\n",
    "\n",
    "coef = pd.Series(lreg.coef_,predictors).sort_values()\n",
    "\n",
    "coef.plot(kind='bar', title='Modal Coefficients')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### After applying the polynomial feature, we see a great improvement in the rmse. As opposite to the dummy version, with ordinal values the polynomial methods works correctly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridreg = Ridge(fit_intercept=True, normalize=False, alpha=0.01)\n",
    "\n",
    "ridreg.fit(X_training, y_training) \n",
    "\n",
    "ridreg_pred = ridreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.8979681373465574 rmse: 1291.293600847612\n"
     ]
    }
   ],
   "source": [
    "rid_r2 = ridreg.score(X_test, y_test)\n",
    "\n",
    "ridreg_rmse = mean_squared_error(y_test, ridreg_pred, sample_weight=None, multioutput='uniform_average', squared=False)\n",
    "\n",
    "print(\"r2:\",rid_r2,\"rmse:\",ridreg_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f68b3f87cd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = X_training.columns\n",
    "\n",
    "coef = pd.Series(ridreg.coef_,predictors).sort_values()\n",
    "\n",
    "coef.plot(kind='bar', title='Modal Coefficients')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "lassreg = Lasso(fit_intercept=True, normalize=True, alpha=0.01)\n",
    "\n",
    "lassreg.fit(X_training, y_training) \n",
    "\n",
    "lassreg_pred = lassreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.8982167179455625 rmse: 1289.7196499534396\n"
     ]
    }
   ],
   "source": [
    "lass_r2 = lassreg.score(X_test, y_test)\n",
    "\n",
    "lassreg_rmse = mean_squared_error(y_test, lassreg_pred, sample_weight=None, multioutput='uniform_average', squared=False)\n",
    "\n",
    "print(\"r2:\",lass_r2,\"rmse:\",lassreg_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f68b2190fd0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = X_training.columns\n",
    "\n",
    "coef = pd.Series(lassreg.coef_,predictors).sort_values()\n",
    "\n",
    "coef.plot(kind='bar', title='Modal Coefficients')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elastic Net Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "enetreg = ElasticNet(fit_intercept=True, normalize=False, alpha=0.01, l1_ratio=0.5)\n",
    "\n",
    "enetreg.fit(X_training, y_training) \n",
    "\n",
    "enetreg_pred = enetreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.8711028722265262 rmse: 1451.3719029504985\n"
     ]
    }
   ],
   "source": [
    "enet_r2 = enetreg.score(X_test, y_test)\n",
    "\n",
    "enet_rmse = mean_squared_error(y_test, enetreg_pred, sample_weight=None, multioutput='uniform_average', squared=False)\n",
    "\n",
    "print(\"r2:\",enet_r2,\"rmse:\",enet_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f68b211b7d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = X_training.columns\n",
    "\n",
    "coef = pd.Series(enetreg.coef_,predictors).sort_values()\n",
    "\n",
    "coef.plot(kind='bar', title='Modal Coefficients')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LARS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LassoLars\n",
    "\n",
    "LARSreg = LassoLars(fit_intercept=True, normalize=False, alpha=0.01)\n",
    "\n",
    "LARSreg.fit(X_training, y_training) \n",
    "\n",
    "LARSreg_pred = LARSreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.897970226615313 rmse: 1291.280380108937\n"
     ]
    }
   ],
   "source": [
    "LARS_r2 = LARSreg.score(X_test, y_test)\n",
    "\n",
    "LARS_rmse = mean_squared_error(y_test, LARSreg_pred, sample_weight=None, multioutput='uniform_average', squared=False)\n",
    "\n",
    "print(\"r2:\",LARS_r2,\"rmse:\",LARS_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f68b209db10>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = X_training.columns\n",
    "\n",
    "coef = pd.Series(LARSreg.coef_,predictors).sort_values()\n",
    "\n",
    "coef.plot(kind='bar', title='Modal Coefficients')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bayessian Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import BayesianRidge\n",
    "\n",
    "bayreg = BayesianRidge()\n",
    "\n",
    "bayreg.fit(X_training, y_training) \n",
    "\n",
    "bayreg_pred = bayreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.8977928935598211 rmse: 1292.4020491883766\n"
     ]
    }
   ],
   "source": [
    "bay_r2 = bayreg.score(X_test, y_test)\n",
    "\n",
    "bay_rmse = mean_squared_error(y_test, bayreg_pred, sample_weight=None, multioutput='uniform_average', squared=False)\n",
    "\n",
    "print(\"r2:\",bay_r2,\"rmse:\",bay_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f68b2023190>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = X_training.columns\n",
    "\n",
    "coef = pd.Series(bayreg.coef_,predictors).sort_values()\n",
    "\n",
    "coef.plot(kind='bar', title='Modal Coefficients')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Robust Regressor: Huber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import HuberRegressor\n",
    "\n",
    "hubreg = HuberRegressor()\n",
    "\n",
    "hubreg.fit(X_training, y_training) \n",
    "\n",
    "hubreg_pred = hubreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.8767686866267873 rmse: 1419.1151285072829\n"
     ]
    }
   ],
   "source": [
    "hub_r2 = hubreg.score(X_test, y_test)\n",
    "\n",
    "hub_rmse = mean_squared_error(y_test, hubreg_pred, sample_weight=None, multioutput='uniform_average', squared=False)\n",
    "\n",
    "print(\"r2:\",hub_r2,\"rmse:\",hub_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f68b1faa810>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = X_training.columns\n",
    "\n",
    "coef = pd.Series(hubreg.coef_,predictors).sort_values()\n",
    "\n",
    "coef.plot(kind='bar', title='Modal Coefficients')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "svrReg = LinearSVR()\n",
    "\n",
    "svrReg.fit(X_training, y_training) \n",
    "\n",
    "svrReg_pred = svrReg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.8158020315281048 rmse: 1735.0001206827405\n"
     ]
    }
   ],
   "source": [
    "svr_r2 = svrReg.score(X_test, y_test)\n",
    "\n",
    "svr_rmse = mean_squared_error(y_test, svrReg_pred, sample_weight=None, multioutput='uniform_average', squared=False)\n",
    "\n",
    "print(\"r2:\",svr_r2,\"rmse:\",svr_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f68b1f2f690>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = X_training.columns\n",
    "\n",
    "coef = pd.Series(svrReg.coef_,predictors).sort_values()\n",
    "\n",
    "coef.plot(kind='bar', title='Modal Coefficients')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see the best option by far is the Polynomial method. So this will be the one we will test in the new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13449, 10)\n"
     ]
    },
    {
     "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>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>4.41</td>\n",
       "      <td>4.43</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>IF</td>\n",
       "      <td>62.1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4.52</td>\n",
       "      <td>4.46</td>\n",
       "      <td>2.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.57</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>60.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>7.55</td>\n",
       "      <td>4.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.31</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>VS2</td>\n",
       "      <td>61.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4.32</td>\n",
       "      <td>4.36</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.51</td>\n",
       "      <td>Good</td>\n",
       "      <td>I</td>\n",
       "      <td>VVS1</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.26</td>\n",
       "      <td>7.21</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  carat        cut color clarity  depth  table     x     y     z\n",
       "0           0   0.30      Ideal     H     SI2   60.0   56.0  4.41  4.43  2.65\n",
       "1           1   0.34      Ideal     D      IF   62.1   57.0  4.52  4.46  2.79\n",
       "2           2   1.57  Very Good     I     VS2   60.3   58.0  7.58  7.55  4.56\n",
       "3           3   0.31      Ideal     H     VS2   61.8   57.0  4.32  4.36  2.68\n",
       "4           4   1.51       Good     I    VVS1   64.0   60.0  7.26  7.21  4.63"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdf = pd.read_csv(\"INPUT/diamonds_test.csv\")\n",
    "print(testdf.shape)\n",
    "testdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute Volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "testdf[\"volume\"] = testdf.x * testdf.y * testdf.z\n",
    "index = testdf[\"Unnamed: 0\"]\n",
    "testdf.drop(columns={\"Unnamed: 0\",\"x\",\"y\",\"z\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ord = testdf\n",
    "\n",
    "cut_dict = {\n",
    "    \"Fair\":1, \n",
    "    \"Good\":2, \n",
    "    \"Very Good\":3, \n",
    "    \"Premium\":4, \n",
    "    \"Ideal\":5\n",
    "}\n",
    "\n",
    "color_dict = {\n",
    "    \"J\":1,\n",
    "    \"I\":2,\n",
    "    \"H\":3, \n",
    "    \"G\":4, \n",
    "    \"F\":5, \n",
    "    \"E\":6, \n",
    "    \"D\":7\n",
    "}\n",
    "\n",
    "clarity_dict = {\n",
    "    \"I1\":1, \n",
    "    \"SI2\":2, \n",
    "    \"SI1\":3, \n",
    "    \"VS2\":4, \n",
    "    \"VS1\":5,\n",
    "    \"VVS2\":6,\n",
    "    \"VVS1\":7,\n",
    "    \"IF\":8\n",
    "}\n",
    "\n",
    "test_ord.cut = test_ord.cut.map(cut_dict)\n",
    "test_ord.color = test_ord.color.map(color_dict)\n",
    "test_ord.clarity = test_ord.clarity.map(clarity_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.30</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>51.771195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.34</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>62.1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>56.244168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.57</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>60.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>260.964240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.31</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>61.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>50.478336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.51</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>242.355498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat  cut  color  clarity  depth  table      volume\n",
       "0   0.30    5      3        2   60.0   56.0   51.771195\n",
       "1   0.34    5      7        8   62.1   57.0   56.244168\n",
       "2   1.57    3      2        4   60.3   58.0  260.964240\n",
       "3   0.31    5      3        4   61.8   57.0   50.478336\n",
       "4   1.51    2      2        7   64.0   60.0  242.355498"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ord.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xss_test = sc_X.fit_transform(test_ord)\n",
    "\n",
    "test_SS = pd.DataFrame(Xss_test, columns=['carat', 'cut', 'color', 'clarity', 'depth', 'table',\n",
    "       'volume'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we train our models again but with 100% of the data and take the best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_pred = polyreg.predict(poly_f.fit_transform(test_SS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'test = pd.DataFrame(test_pred, columns={\"price\"})\\ntest[\"id\"] = index\\ntest = test[[\"id\",\"price\"]]'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"test = pd.DataFrame(test_pred, columns={\"price\"})\n",
    "test[\"id\"] = index\n",
    "test = test[[\"id\",\"price\"]]\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test.to_csv(\"OUTPUT/polynomial_ordinal.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As conclusion, we have tested the Polynomial version in new data and the rmse increased from 630 in our test to 965, probably due to the presence of outliers"
   ]
  }
 ],
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