STAT666-016 Final Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original size  4177\n",
      "size after dropna size  4177\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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole Weight</th>\n",
       "      <th>Shucked Weight</th>\n",
       "      <th>Viscera Weight</th>\n",
       "      <th>Shell Weight</th>\n",
       "      <th>Rings</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex  Length  Diameter  Height  Whole Weight  Shucked Weight  \\\n",
       "0    1   0.455     0.365   0.095        0.5140          0.2245   \n",
       "1    1   0.350     0.265   0.090        0.2255          0.0995   \n",
       "2    2   0.530     0.420   0.135        0.6770          0.2565   \n",
       "3    1   0.440     0.365   0.125        0.5160          0.2155   \n",
       "4    3   0.330     0.255   0.080        0.2050          0.0895   \n",
       "\n",
       "   Viscera Weight  Shell Weight  Rings   Age  \n",
       "0          0.1010         0.150     15  16.5  \n",
       "1          0.0485         0.070      7   8.5  \n",
       "2          0.1415         0.210      9  10.5  \n",
       "3          0.1140         0.155     10  11.5  \n",
       "4          0.0395         0.055      7   8.5  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the Pandas libraries with alias 'pd' \n",
    "\n",
    "# Load dataset\n",
    "# Read data from file 'http://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data' \n",
    "data = \"http://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data\"\n",
    "names = ['Sex', 'Length', 'Diameter', 'Height', 'Whole Weight', 'Shucked Weight', 'Viscera Weight', 'Shell Weight', 'Rings']\n",
    "df = pd.read_csv(data, names=names)\n",
    "\n",
    "# Ignore the data for which the height is zero\n",
    "df = df[df['Height'] > 0]\n",
    "\n",
    "# Map sex to int\n",
    "sex_mapping = {'M': 1, 'F': 2, 'I': 3}\n",
    "df['Sex'] = df['Sex'].map(sex_mapping)\n",
    "\n",
    "df['Age'] = df['Rings'] + 1.5\n",
    "\n",
    "# Check for NAN values - this dataset doesn't have any since it's run by UCI it's already 'clean'\n",
    "original_size = len(X)\n",
    "X = X.dropna()\n",
    "removed_size = len(X)\n",
    "\n",
    "print('original size ', original_size)\n",
    "print('size after dropna size ', removed_size)\n",
    "\n",
    "df.head() # just gives you a peak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "y = df['Rings'].tolist() # values we are trying to predict\n",
    "X = df.drop(['Rings','Age'], axis=1) # rings is what we are predicting and sex was\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Dave/Library/Python/3.6/lib/python/site-packages/scipy/linalg/basic.py:1226: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
      "  warnings.warn(mesg, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4831688142479189"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 1. Set up the model\n",
    "model = LinearRegression()\n",
    "\n",
    "# 2. Use fit\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 3. Check the score\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = model.predict(X_test) # Predict unkown data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph predicted vs actual values\n",
    "import numpy as np\n",
    "\n",
    "plt.plot(y_test, y_predict, '.')\n",
    "\n",
    "# plot a line, a perfect prediction would all fall on this line y = x\n",
    "x = np.linspace(0, 30, 100)\n",
    "y = x\n",
    "plt.plot(x, y)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_error_percentage(y_predict, y_test):\n",
    "    # If this doesn't make sense just take the code out of the function definition above\n",
    "\n",
    "    if len(y_predict) != len(y_test):\n",
    "        # have to be the same length so we can match them up\n",
    "        print(\"ERROR: predicted and test values are not the same length!\")\n",
    "        return\n",
    "\n",
    "    error_values = []\n",
    "\n",
    "    for index, predicted_val in enumerate(y_predict):\n",
    "        actual_val = y_test[index] # y_test is the actual value\n",
    "        error = abs(predicted_val - actual_val) / actual_val # calculate error as described in directions\n",
    "\n",
    "        error_values.append(error) # add the error to our list\n",
    "\n",
    "    return error_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_list = calculate_error_percentage(y_predict, y_test)\n",
    "error_df = pd.DataFrame(error_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1253.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.161516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.153120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.062943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.130376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.225705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.083417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "count  1253.000000\n",
       "mean      0.161516\n",
       "std       0.153120\n",
       "min       0.000296\n",
       "25%       0.062943\n",
       "50%       0.130376\n",
       "75%       0.225705\n",
       "max       3.083417"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4831688142479189"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html\n",
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27465294418817243"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# http://benalexkeen.com/linear-regression-in-python-using-scikit-learn/\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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