finding_donors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from time import time\n",
    "from IPython.display import display\n",
    "\n",
    "import visuals as vs\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass education_level  education-num       marital-status  \\\n",
       "0   39          State-gov       Bachelors           13.0        Never-married   \n",
       "1   50   Self-emp-not-inc       Bachelors           13.0   Married-civ-spouse   \n",
       "2   38            Private         HS-grad            9.0             Divorced   \n",
       "3   53            Private            11th            7.0   Married-civ-spouse   \n",
       "4   28            Private       Bachelors           13.0   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race      sex  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male        2174.0   \n",
       "1     Exec-managerial         Husband   White     Male           0.0   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male           0.0   \n",
       "3   Handlers-cleaners         Husband   Black     Male           0.0   \n",
       "4      Prof-specialty            Wife   Black   Female           0.0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country income  \n",
       "0           0.0            40.0   United-States  <=50K  \n",
       "1           0.0            13.0   United-States  <=50K  \n",
       "2           0.0            40.0   United-States  <=50K  \n",
       "3           0.0            40.0   United-States  <=50K  \n",
       "4           0.0            40.0            Cuba  <=50K  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"census.csv\")\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of records: 45222\n",
      "Individuals making more than $50,000: 11208\n",
      "Individuals making at most $50,000: 34014\n",
      "Percentage of individuals making more than $50,000: 24.78439697492371%\n"
     ]
    }
   ],
   "source": [
    "# TODO: Total number of records\n",
    "n_records = len(df)\n",
    "\n",
    "# TODO: Number of records where individual's income is more than $50,000\n",
    "n_greater_50k = len(df[df.income=='>50K'])\n",
    "\n",
    "# TODO: Number of records where individual's income is at most $50,000\n",
    "n_at_most_50k = len(df[df.income=='<=50K'])\n",
    "\n",
    "# TODO: Percentage of individuals whose income is more than $50,000\n",
    "greater_percent = float(n_greater_50k/n_records*100.0)\n",
    "\n",
    "# Print the results\n",
    "print(\"Total number of records: {}\".format(n_records))\n",
    "print(\"Individuals making more than $50,000: {}\".format(n_greater_50k))\n",
    "print(\"Individuals making at most $50,000: {}\".format(n_at_most_50k))\n",
    "print(\"Percentage of individuals making more than $50,000: {}%\".format(greater_percent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label=df['income']\n",
    "features=df.drop('income',axis=1)\n",
    "vs.distribution(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Log-transform the skewed features\n",
    "skewed = ['capital-gain', 'capital-loss']\n",
    "features[skewed] = df[skewed].apply(lambda x: np.log(x + 1))\n",
    "\n",
    "# Visualize the new log distributions\n",
    "vs.distribution(features, transformed = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bahaa/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.301370</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.02174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.452055</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.287671</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.493151</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.150685</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age          workclass education_level  education-num  \\\n",
       "0  0.301370          State-gov       Bachelors       0.800000   \n",
       "1  0.452055   Self-emp-not-inc       Bachelors       0.800000   \n",
       "2  0.287671            Private         HS-grad       0.533333   \n",
       "3  0.493151            Private            11th       0.400000   \n",
       "4  0.150685            Private       Bachelors       0.800000   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  \n",
       "0       0.02174           0.0        0.397959   United-States  \n",
       "1       0.00000           0.0        0.122449   United-States  \n",
       "2       0.00000           0.0        0.397959   United-States  \n",
       "3       0.00000           0.0        0.397959   United-States  \n",
       "4       0.00000           0.0        0.397959            Cuba  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import sklearn.preprocessing.StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Initialize a scaler, then apply it to the features\n",
    "scaler = MinMaxScaler()\n",
    "numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
    "features[numerical] = scaler.fit_transform(df[numerical])\n",
    "\n",
    "# Show an example of a record with scaling applied\n",
    "display(features.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103 total features after one-hot encoding.\n",
      "['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week', 'workclass_ Federal-gov', 'workclass_ Local-gov', 'workclass_ Private', 'workclass_ Self-emp-inc', 'workclass_ Self-emp-not-inc', 'workclass_ State-gov', 'workclass_ Without-pay', 'education_level_ 10th', 'education_level_ 11th', 'education_level_ 12th', 'education_level_ 1st-4th', 'education_level_ 5th-6th', 'education_level_ 7th-8th', 'education_level_ 9th', 'education_level_ Assoc-acdm', 'education_level_ Assoc-voc', 'education_level_ Bachelors', 'education_level_ Doctorate', 'education_level_ HS-grad', 'education_level_ Masters', 'education_level_ Preschool', 'education_level_ Prof-school', 'education_level_ Some-college', 'marital-status_ Divorced', 'marital-status_ Married-AF-spouse', 'marital-status_ Married-civ-spouse', 'marital-status_ Married-spouse-absent', 'marital-status_ Never-married', 'marital-status_ Separated', 'marital-status_ Widowed', 'occupation_ Adm-clerical', 'occupation_ Armed-Forces', 'occupation_ Craft-repair', 'occupation_ Exec-managerial', 'occupation_ Farming-fishing', 'occupation_ Handlers-cleaners', 'occupation_ Machine-op-inspct', 'occupation_ Other-service', 'occupation_ Priv-house-serv', 'occupation_ Prof-specialty', 'occupation_ Protective-serv', 'occupation_ Sales', 'occupation_ Tech-support', 'occupation_ Transport-moving', 'relationship_ Husband', 'relationship_ Not-in-family', 'relationship_ Other-relative', 'relationship_ Own-child', 'relationship_ Unmarried', 'relationship_ Wife', 'race_ Amer-Indian-Eskimo', 'race_ Asian-Pac-Islander', 'race_ Black', 'race_ Other', 'race_ White', 'sex_ Female', 'sex_ Male', 'native-country_ Cambodia', 'native-country_ Canada', 'native-country_ China', 'native-country_ Columbia', 'native-country_ Cuba', 'native-country_ Dominican-Republic', 'native-country_ Ecuador', 'native-country_ El-Salvador', 'native-country_ England', 'native-country_ France', 'native-country_ Germany', 'native-country_ Greece', 'native-country_ Guatemala', 'native-country_ Haiti', 'native-country_ Holand-Netherlands', 'native-country_ Honduras', 'native-country_ Hong', 'native-country_ Hungary', 'native-country_ India', 'native-country_ Iran', 'native-country_ Ireland', 'native-country_ Italy', 'native-country_ Jamaica', 'native-country_ Japan', 'native-country_ Laos', 'native-country_ Mexico', 'native-country_ Nicaragua', 'native-country_ Outlying-US(Guam-USVI-etc)', 'native-country_ Peru', 'native-country_ Philippines', 'native-country_ Poland', 'native-country_ Portugal', 'native-country_ Puerto-Rico', 'native-country_ Scotland', 'native-country_ South', 'native-country_ Taiwan', 'native-country_ Thailand', 'native-country_ Trinadad&Tobago', 'native-country_ United-States', 'native-country_ Vietnam', 'native-country_ Yugoslavia']\n"
     ]
    }
   ],
   "source": [
    "final_features=pd.get_dummies(features)\n",
    "final_label=label.apply(lambda x:1 if x== \">50K\" else 0)\n",
    "encoded = list(final_features.columns)\n",
    "print (\"{} total features after one-hot encoding.\".format(len(encoded)))\n",
    "\n",
    "print (encoded)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 36177 samples.\n",
      "Testing set has 9045 samples.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(final_features,final_label,test_size=0.2,random_state=0)\n",
    "print(\"Training set has {} samples.\".format(X_train.shape[0]))\n",
    "print(\"Testing set has {} samples.\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 - Naive Predictor Performace\n",
    "\n",
    "If we chose a model that always predicted an individual made more than $50,000, what would that model's accuracy and F-score be on this dataset? You must use the code cell below and assign your results to 'accuracy' and 'fscore' to be used later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "TP = np.sum(income) # Counting the ones as this is the naive case. Note that 'income' is the 'income_raw' data \n",
    "encoded to numerical values done in the data preprocessing step.\n",
    "FP = income.count() - TP # Specific to the naive case\n",
    "\n",
    "TN = 0 # No predicted negatives in the naive case\n",
    "FN = 0 # No predicted negatives in the naive case\n",
    "'''\n",
    "# TODO: Calculate accuracy, precision and recall\n",
    "TP = np.sum(final_label)\n",
    "FP=final_label.count()-TP\n",
    "TN=0\n",
    "FN=0\n",
    "precision = float(TP)/(TP+FP)\n",
    "recall = float(TP)/(TP+FN)\n",
    "accuracy = precision \n",
    "\n",
    "# TODO: Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.\n",
    "fscore = (1+0.5**2)*(precision*recall)/(0.5**2*precision+recall)\n",
    "\n",
    "# Print the results \n",
    "print(\"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]\".format(accuracy, fscore))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2 - Model Application\n",
    "List three of the supervised learning models above that are appropriate for this problem that you will test on the census data. For each model chosen\n",
    "\n",
    "Describe one real-world application in industry where the model can be applied.\n",
    "What are the strengths of the model; when does it perform well?\n",
    "What are the weaknesses of the model; when does it perform poorly?\n",
    "What makes this model a good candidate for the problem, given what you know about the data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer :\n",
    "\n",
    "1- Decision Trees\n",
    "\n",
    "- Real world application: Decision Trees and, in general, CART (Classification and Regression Trees) are often used in financial analysis. A concrete example of it is: for predicting which stocks to buy based on past peformance. \n",
    "- Strengths: Able to handle categorical and numerical data. Doesn’t require much data pre-processing, and can handle data which hasn’t been normalized, or encoded for Machine Learning Suitability. Simple to understand and interpret.\n",
    "- Weaknesses: Complex Decision Trees do not generalize well to the data and can result in overfitting. Unstable, as small variations in the data can result in a different decision tree. Hence they are usually used in an ensemble (like Random Forests) to build robustness. Can create biased trees if some classes dominate.\n",
    "- Candidacy: Since a decision tree can handle both numerical and categorical data, it’s a good candidate for our case (although, the pre-processing steps might already mitigate whatever advantage we would have had). It’s also easy to interpret, so we will know what happens under the hood to interpret the results.\n",
    "\n",
    "2- Support Vector Machines (SVM)\n",
    "\n",
    "- Real world application: Example of a real world use of SVMs include image classification and image segmentation. For example: Face detection in an image. \n",
    "- Strenghs: Effective in high dimensional spaces, or when there are a lot of features. Kernel functions can be used to adapt to different cases, and can be completely customized if needed. Thus SVMs are versatile.\n",
    "- Weaknesses: Doesn’t perform well with large datasets. Doesn’t directly provide probability estimates.\n",
    "- Candidacy: SVMs were chosen because of their effectiveness given high dimensionality. After incorporating dummy variables, we have more than 100 features in our dataset, so SVMs should be a classifier that works regardless of that. Also, our dataset is not that large to be a deterrent.\n",
    "\n",
    "3- Ensemble methods: AdaBoost\n",
    "\n",
    "- Real world application: Ensemble methods are used extensively in Kaggle competitions, usually in image detection. A real world example of Adaboost is object detection in image, ex: identifying players during a game of basketball. \n",
    "- Strength: Ensemble methods, including Adaboost are more robust than single estimators, have improved generalizability. Simple models can be combined to build a complex model, which is computationally fast.\n",
    "- Weaknesses: If we have a biased underlying classifier, it will lead to a biased boosted model.\n",
    "- Candidacy: Ensemble methods are considered to be high quality classifiers, and adaboost is the one of most popular boosting algorithms. We also have a class imbalance in our dataset, which boosting might be robust to\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Import two metrics from sklearn - fbeta_score and accuracy_score\n",
    "from sklearn.metrics import fbeta_score, accuracy_score\n",
    "\n",
    "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): \n",
    "    '''\n",
    "    inputs:\n",
    "       - learner: the learning algorithm to be trained and predicted on\n",
    "       - sample_size: the size of samples (number) to be drawn from training set\n",
    "       - X_train: features training set\n",
    "       - y_train: income training set\n",
    "       - X_test: features testing set\n",
    "       - y_test: income testing set\n",
    "    '''\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    # TODO: Fit the learner to the training data using slicing with 'sample_size' using .fit(training_features[:], training_labels[:])\n",
    "    start = time() # Get start time\n",
    "    learner.fit(X_train[:sample_size],y_train[:sample_size])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # TODO: Calculate the training time\n",
    "    results['train_time'] = end-start\n",
    "        \n",
    "    # TODO: Get the predictions on the test set(X_test),\n",
    "    #       then get predictions on the first 300 training samples(X_train) using .predict()\n",
    "    start = time() # Get start time\n",
    "    predictions_test = learner.predict(X_test)\n",
    "    predictions_train = learner.predict(X_train[:300])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # TODO: Calculate the total prediction time\n",
    "    results['pred_time'] = end-start\n",
    "            \n",
    "    # TODO: Compute accuracy on the first 300 training samples which is y_train[:300]\n",
    "    results['acc_train'] = accuracy_score(y_train[:300],predictions_train)\n",
    "        \n",
    "    # TODO: Compute accuracy on test set using accuracy_score()\n",
    "    results['acc_test'] = accuracy_score(y_test,predictions_test)\n",
    "    \n",
    "    # TODO: Compute F-score on the the first 300 training samples using fbeta_score()\n",
    "    results['f_train'] = fbeta_score(y_train[:300],predictions_train,beta=0.5)\n",
    "        \n",
    "    # TODO: Compute F-score on the test set which is y_test\n",
    "    results['f_test'] = fbeta_score(y_test,predictions_test,beta=0.5)\n",
    "       \n",
    "    # Success\n",
    "    print(\"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size))\n",
    "        \n",
    "    # Return the results\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier trained on 362 samples.\n",
      "DecisionTreeClassifier trained on 3618 samples.\n",
      "DecisionTreeClassifier trained on 36177 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bahaa/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bahaa/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/home/bahaa/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC trained on 362 samples.\n",
      "SVC trained on 3618 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bahaa/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC trained on 36177 samples.\n",
      "AdaBoostClassifier trained on 362 samples.\n",
      "AdaBoostClassifier trained on 3618 samples.\n",
      "AdaBoostClassifier trained on 36177 samples.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "# TODO: Initialize the three models, the random states are set to 101 so we know how to reproduce the model later\n",
    "clf_A = DecisionTreeClassifier(random_state=101)\n",
    "clf_B = SVC(random_state = 101)\n",
    "clf_C = AdaBoostClassifier(random_state = 101)\n",
    "\n",
    "# TODO: Calculate the number of samples for 1%, 10%, and 100% of the training data\n",
    "samples_1 = int(round(len(X_train) / 100))\n",
    "samples_10 = int(round(len(X_train) / 10))\n",
    "samples_100 = len(X_train)\n",
    "\n",
    "# Collect results on the learners\n",
    "results = {}\n",
    "for clf in [clf_A, clf_B, clf_C]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    results[clf_name] = {}\n",
    "    for i, samples in enumerate([samples_1, samples_10, samples_100]):\n",
    "        results[clf_name][i] = \\\n",
    "        train_predict(clf, samples, X_train, y_train, X_test, y_test)\n",
    "\n",
    "# Run metrics visualization for the three supervised learning models chosen\n",
    "vs.evaluate(results, accuracy, fscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1%</th>\n",
       "      <th>10%</th>\n",
       "      <th>100%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>acc_test</th>\n",
       "      <td>0.754008</td>\n",
       "      <td>0.806081</td>\n",
       "      <td>0.817026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_train</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996667</td>\n",
       "      <td>0.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_test</th>\n",
       "      <td>0.505562</td>\n",
       "      <td>0.602673</td>\n",
       "      <td>0.624711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_train</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997191</td>\n",
       "      <td>0.963855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pred_time</th>\n",
       "      <td>0.005067</td>\n",
       "      <td>0.005759</td>\n",
       "      <td>0.005379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_time</th>\n",
       "      <td>0.004786</td>\n",
       "      <td>0.030633</td>\n",
       "      <td>0.381173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  1%       10%      100%\n",
       "acc_test    0.754008  0.806081  0.817026\n",
       "acc_train   1.000000  0.996667  0.970000\n",
       "f_test      0.505562  0.602673  0.624711\n",
       "f_train     1.000000  0.997191  0.963855\n",
       "pred_time   0.005067  0.005759  0.005379\n",
       "train_time  0.004786  0.030633  0.381173"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC\n"
     ]
    },
    {
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       "    </tr>\n",
       "  </thead>\n",
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       "      <th>acc_test</th>\n",
       "      <td>0.756219</td>\n",
       "      <td>0.822443</td>\n",
       "      <td>0.830072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_train</th>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.836667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_test</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.640209</td>\n",
       "      <td>0.659238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_train</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.669643</td>\n",
       "      <td>0.677966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pred_time</th>\n",
       "      <td>0.283756</td>\n",
       "      <td>2.665637</td>\n",
       "      <td>21.979537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_time</th>\n",
       "      <td>0.013850</td>\n",
       "      <td>1.271203</td>\n",
       "      <td>130.845841</td>\n",
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       "                  1%       10%        100%\n",
       "acc_test    0.756219  0.822443    0.830072\n",
       "acc_train   0.760000  0.833333    0.836667\n",
       "f_test      0.000000  0.640209    0.659238\n",
       "f_train     0.000000  0.669643    0.677966\n",
       "pred_time   0.283756  2.665637   21.979537\n",
       "train_time  0.013850  1.271203  130.845841"
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostClassifier\n"
     ]
    },
    {
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       "      <th>1%</th>\n",
       "      <th>10%</th>\n",
       "      <th>100%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>acc_test</th>\n",
       "      <td>0.810392</td>\n",
       "      <td>0.849862</td>\n",
       "      <td>0.857601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_train</th>\n",
       "      <td>0.896667</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_test</th>\n",
       "      <td>0.610473</td>\n",
       "      <td>0.701882</td>\n",
       "      <td>0.724551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_train</th>\n",
       "      <td>0.811688</td>\n",
       "      <td>0.680147</td>\n",
       "      <td>0.711538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pred_time</th>\n",
       "      <td>0.059820</td>\n",
       "      <td>0.066278</td>\n",
       "      <td>0.057709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_time</th>\n",
       "      <td>0.048687</td>\n",
       "      <td>0.282600</td>\n",
       "      <td>1.449787</td>\n",
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       "                  1%       10%      100%\n",
       "acc_test    0.810392  0.849862  0.857601\n",
       "acc_train   0.896667  0.840000  0.850000\n",
       "f_test      0.610473  0.701882  0.724551\n",
       "f_train     0.811688  0.680147  0.711538\n",
       "pred_time   0.059820  0.066278  0.057709\n",
       "train_time  0.048687  0.282600  1.449787"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#More details Than visualization\n",
    "for i in results.items():\n",
    "    print (i[0])\n",
    "    display(pd.DataFrame(i[1]).rename(columns={0:'1%', 1:'10%', 2:'100%'}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3 - Choosing the Best Model\n",
    "Based on the evaluation you performed earlier, in one to two paragraphs, explain to CharityML which of the three models you believe to be most appropriate for the task of identifying individuals that make more than $50,000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer :\n",
    "\n",
    "First and foremost, it is the classifier that performs the best on the testing data, in terms of both the accuracy and f-score. It also takes resonably low time to train on the full dataset, which is just a fraction of the 120 seconds taken by SVM, the next best classifier to train on the full training set. So it should scale well even if we have more data.\n",
    "\n",
    "By default, Adaboost uses a decision stump i.e. a decision tree of depth 1 as its base classifier, which can handle categorical and numerical data. Weak learners are relatively faster to train, so the dataset size is not a problem for the algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4 - Describing the Model in Layman's Terms\n",
    "\n",
    "* In one to two paragraphs, explain to *CharityML*, in layman's terms, how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical jargon, such as describing equations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer :\n",
    "    \n",
    "Adaboost works by combining several simple learners (for ex: decision trees), to create an ensemble of learners that can predict whether an individual earns above 50k or not.\n",
    "\n",
    "The Adaboost combines weak learner a.k.a. base learner (D1, D2 and D3) to form a strong rule.\n",
    "\n",
    "Step 1: The base learner takes all the distributions and assign equal weight (D1) or attention to each observation.\n",
    "\n",
    "Step 2: If there is any prediction error caused by first base learning algorithm, then we pay higher attention to observations having prediction error. Then, we apply the next base learning algorithm.\n",
    "\n",
    "Step 3: Iterate Step 2 till the limit of base learning algorithm is reached or higher accuracy is achieved.\n",
    "\n",
    "Finally, it combines the outputs from weak learner and creates a strong learner which eventually improves the prediction power of the model. Boosting pays higher focus on examples which are mis-classiﬁed or have higher errors by preceding weak rules.\n",
    "![](Adaboost.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bahaa/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.8353\n",
      "F-score on testing data: 0.6640\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.8702\n",
      "Final F-score on the testing data: 0.7526\n",
      "AdaBoostClassifier(algorithm='SAMME.R',\n",
      "          base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=6,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "            splitter='best'),\n",
      "          learning_rate=0.5, n_estimators=50, random_state=None)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# TODO: Initialize the classifier\n",
    "clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(random_state=101))\n",
    "\n",
    "# TODO: Create the parameters list you wish to tune\n",
    "parameters = {'n_estimators':[50, 120], \n",
    "              'learning_rate':[0.1, 0.5, 1.],\n",
    "              'base_estimator__min_samples_split' : np.arange(2, 8, 2),\n",
    "              'base_estimator__max_depth' : np.arange(1, 4, 1)\n",
    "             }\n",
    "\n",
    "# TODO: Make an fbeta_score scoring object\n",
    "scorer = make_scorer(fbeta_score,beta=0.5)\n",
    "\n",
    "# TODO: Perform grid search on the classifier using 'scorer' as the scoring method\n",
    "grid_obj = GridSearchCV(clf, parameters,scorer)\n",
    "\n",
    "# TODO: Fit the grid search object to the training data and find the optimal parameters\n",
    "grid_fit = grid_obj.fit(X_train,y_train)\n",
    "\n",
    "# Get the estimator\n",
    "best_clf = grid_fit.best_estimator_\n",
    "\n",
    "# Make predictions using the unoptimized and model\n",
    "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
    "best_predictions = best_clf.predict(X_test)\n",
    "\n",
    "# Report the before-and-afterscores\n",
    "print (\"Unoptimized model\\n------\")\n",
    "print (\"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions)))\n",
    "print (\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5)))\n",
    "print (\"\\nOptimized Model\\n------\")\n",
    "print (\"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions)))\n",
    "print (\"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5)))\n",
    "print (best_clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5 - Final Model Evaluation\n",
    "\n",
    "* What is your optimized model's accuracy and F-score on the testing data? \n",
    "* Are these scores better or worse than the unoptimized model? \n",
    "* How do the results from your optimized model compare to the naive predictor benchmarks you found earlier in **Question 1**?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results:\n",
    "\n",
    "|     Metric     |Benchmark Predictor| Unoptimized Model| Optimized Model |\n",
    "| :------------: | :---------------: |:---------------: | :-------------: | \n",
    "| Accuracy Score |        0.2478\t |        0.8353    |     0.8702      |\n",
    "| F-score        |         0.2917\t |        0.6640    |     0.7502      |\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer :\n",
    "    \n",
    "The optimized model has an accuracy of 0.8702 and F-score of 0.7526.\n",
    "\n",
    "These scores are better than the umpotimized model, while being substantially better than the benchmark predictor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6 - Feature Relevance Observation\n",
    "When **Exploring the Data**, it was shown there are thirteen available features for each individual on record in the census data. Of these thirteen records, which five features do you believe to be most important for prediction, and in what order would you rank them and why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer :\n",
    "\n",
    "1- capital-gain - measures how much profit an individual is making, should be greatly dependent on income.\n",
    "\n",
    "2- capital-loss - total loss suffered by an individual.\n",
    "\n",
    "3- education-num - would provide some insight into how educated an individual is, higher the education, higher could be the income.\n",
    "\n",
    "4- occupation - income depends on the type of occupation and field where the individual is working.\n",
    "\n",
    "5- age - more experienced/older people could be earning higher compared to younger people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = AdaBoostClassifier(random_state=543)\n",
    "# TODO: Train the supervised model on the training set \n",
    "model = clf.fit(X_train,y_train)\n",
    "\n",
    "# TODO: Extract the feature importances\n",
    "importances = clf.feature_importances_\n",
    "\n",
    "vs.feature_plot(importances, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7 - Extracting Feature Importance\n",
    "\n",
    "Observe the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above \\$50,000.  \n",
    "* How do these five features compare to the five features you discussed in **Question 6**?\n",
    "* If you were close to the same answer, how does this visualization confirm your thoughts? \n",
    "* If you were not close, why do you think these features are more relevant?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:\n",
    "\n",
    "The features identified by the Adaboost algorithm are the same features identify in Question 6 except the education_level_bachelors variable. The 5th feature identified in Question 6 was occupation. However, occupation and education_level_bachelors are probably strongly correlated. So it's not surprising that education_level_Bachelors was identified instead of occupation. The other surprise is that based on Adaboost, the most relevant feature is capital-loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.8702\n",
      "F-score on testing data: 0.7526\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.8437\n",
      "F-score on testing data: 0.7065\n"
     ]
    }
   ],
   "source": [
    "# Import functionality for cloning a model\n",
    "from sklearn.base import clone\n",
    "\n",
    "# Reduce the feature space\n",
    "X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "X_test_reduced = X_test[X_test.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "\n",
    "# Train on the \"best\" model found from grid search earlier\n",
    "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n",
    "\n",
    "# Make new predictions\n",
    "reduced_predictions = clf.predict(X_test_reduced)\n",
    "\n",
    "# Report scores from the final model using both versions of data\n",
    "print (\"Final Model trained on full data\\n------\")\n",
    "print (\"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions)))\n",
    "print (\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5)))\n",
    "print (\"\\nFinal Model trained on reduced data\\n------\")\n",
    "print (\"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, reduced_predictions)))\n",
    "print (\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, reduced_predictions, beta = 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 8 - Effects of Feature Selection\n",
    "\n",
    "* How does the final model's F-score and accuracy score on the reduced data using only five features compare to those same scores when all features are used?\n",
    "* If training time was a factor, would you consider using the reduced data as your training set?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer :\n",
    "\n",
    "The final model's F-score and accuracy on the reduced data are lower than the model including all the features. It went down from 0.8676 to 0.8302 for the accuracy and 0.7448 to 0.6733 for F-score. If it was a real time application, I would consider the model on the reduced data. However, in the case of CharityML because the F-score has decreased by more than 7%, I would suggest to used the model built based on the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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