diff --git "a/AdultNoteBook/Kernels/AdaBoost/.ipynb_checkpoints/0-income-prediction-84-369-accuracy-checkpoint.ipynb" "b/AdultNoteBook/Kernels/AdaBoost/.ipynb_checkpoints/0-income-prediction-84-369-accuracy-checkpoint.ipynb"
new file mode 100644--- /dev/null
+++ "b/AdultNoteBook/Kernels/AdaBoost/.ipynb_checkpoints/0-income-prediction-84-369-accuracy-checkpoint.ipynb"
@@ -0,0 +1,1183 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "26ca1808-47be-4291-bae6-aee31907dfd0",
+    "_execution_state": "idle",
+    "_uuid": "8b5645b4fc767f691c88c0beeb918337c49b6433"
+   },
+   "source": [
+    "#Income Prediction Problem\n",
+    "In this Notebook, I am working through the Income Prediction problem associated with the Adult Income Census dataset. The goal is to accurately predict whether or not someone is making more or less than $50,000 a year. While working through this problem, I am following a framework I use to attack all my machine learning problems. It includes the following steps:\n",
+    "\n",
+    "1. Load Libraries\n",
+    "2. Load Data\n",
+    "3. Analyze Data\n",
+    "4. Feature Engineering\n",
+    "5. Modeling\n",
+    "6. Algorithm Tuning\n",
+    "7. Finalizing the Model\n",
+    "\n",
+    "I hope you enjoy this notebook and find it useful. Please keep in mind this is my first Notebook on here so don't judge it too harshly!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "2249cd4d-d941-4015-8103-9dcdec8bc587",
+    "_execution_state": "idle",
+    "_uuid": "b36102e03c9ec96cd20c1f6d40a1f8591d45c0c6"
+   },
+   "source": [
+    "##1. Load Libaraies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "bfca2f2a-d078-4f0a-ad8a-3d3571a08f76",
+    "_execution_state": "idle",
+    "_uuid": "e5e4b5ee88062058ceff62de7993eac563c1582b"
+   },
+   "source": [
+    "First, we need to load all of our libraries we will use for this project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "_cell_guid": "9201e3be-d4ec-4e38-9e7b-eb7bf54d7c25",
+    "_execution_state": "idle",
+    "_uuid": "de4cbbff6d2b51eb47a5a4719da64cd27804989d"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from collections import Counter\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve, train_test_split, KFold\n",
+    "from sklearn.metrics import classification_report\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "sns.set(style='white', context='notebook', palette='deep')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "bf1d51ca-c701-44dd-a305-dbbfd4bb0cec",
+    "_execution_state": "idle",
+    "_uuid": "161e57d369bf62b7ad27912cc4ae0116ac5de0b6"
+   },
+   "source": [
+    "##2. Load Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "960b99a1-4408-4c1a-b86b-788c17e3b18b",
+    "_execution_state": "idle",
+    "_uuid": "0ecc4df26f5b56f9bfc4ed1841a73c8aafa00837"
+   },
+   "source": [
+    "Next, we load our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "_cell_guid": "946806b5-f230-4216-b42e-a8358e51b605",
+    "_execution_state": "idle",
+    "_uuid": "c4141c604b71fa95c187aa09fe7bed39106fcb1d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age               0\n",
+       "workclass         0\n",
+       "fnlwgt            0\n",
+       "education         0\n",
+       "education.num     0\n",
+       "marital.status    0\n",
+       "occupation        0\n",
+       "relationship      0\n",
+       "race              0\n",
+       "sex               0\n",
+       "capital.gain      0\n",
+       "capital.loss      0\n",
+       "hours.per.week    0\n",
+       "native.country    0\n",
+       "income            0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = pd.read_csv(\"../input/adult.csv\")\n",
+    "\n",
+    "# Check for Null Data\n",
+    "dataset.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "_cell_guid": "25324eeb-2942-45fe-afae-0532421929cb",
+    "_execution_state": "idle",
+    "_uuid": "771a425b9b2a13c2266d851158fbc9e53211e118"
+   },
+   "outputs": [],
+   "source": [
+    "# Replace All Null Data in NaN\n",
+    "dataset = dataset.fillna(np.nan)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "_cell_guid": "86a2ec1f-d7e8-4f29-a949-68240fe69d87",
+    "_execution_state": "idle",
+    "_uuid": "68a3687580bd65fb09a100d49cecfe1774df79dc"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age                int64\n",
+       "workclass         object\n",
+       "fnlwgt             int64\n",
+       "education         object\n",
+       "education.num      int64\n",
+       "marital.status    object\n",
+       "occupation        object\n",
+       "relationship      object\n",
+       "race              object\n",
+       "sex               object\n",
+       "capital.gain       int64\n",
+       "capital.loss       int64\n",
+       "hours.per.week     int64\n",
+       "native.country    object\n",
+       "income            object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get data types\n",
+    "dataset.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "_cell_guid": "1cbf148a-b814-40a3-9154-70c4511bdcd2",
+    "_execution_state": "idle",
+    "_uuid": "5fd35d640fdd346662ac08f7ab16ab996dfeb864"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\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>fnlwgt</th>\n",
+       "      <th>education</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>90</td>\n",
+       "      <td>?</td>\n",
+       "      <td>77053</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>82</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>132870</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>Exec-managerial</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>18</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>66</td>\n",
+       "      <td>?</td>\n",
+       "      <td>186061</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>54</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>140359</td>\n",
+       "      <td>7th-8th</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Machine-op-inspct</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age workclass  fnlwgt     education  education.num marital.status  \\\n",
+       "0   90         ?   77053       HS-grad              9        Widowed   \n",
+       "1   82   Private  132870       HS-grad              9        Widowed   \n",
+       "2   66         ?  186061  Some-college             10        Widowed   \n",
+       "3   54   Private  140359       7th-8th              4       Divorced   \n",
+       "\n",
+       "          occupation   relationship   race     sex  capital.gain  \\\n",
+       "0                  ?  Not-in-family  White  Female             0   \n",
+       "1    Exec-managerial  Not-in-family  White  Female             0   \n",
+       "2                  ?      Unmarried  Black  Female             0   \n",
+       "3  Machine-op-inspct      Unmarried  White  Female             0   \n",
+       "\n",
+       "   capital.loss  hours.per.week native.country income  \n",
+       "0          4356              40  United-States  <=50K  \n",
+       "1          4356              18  United-States  <=50K  \n",
+       "2          4356              40  United-States  <=50K  \n",
+       "3          3900              40  United-States  <=50K  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Peek at data\n",
+    "dataset.head(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "_cell_guid": "c183daab-b3b2-4cab-91a0-05f0b7a98dce",
+    "_execution_state": "idle",
+    "_uuid": "0b0c1b473300ee13c423318626c371835d032e12"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\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>fnlwgt</th>\n",
+       "      <th>education</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>90</td>\n",
+       "      <td>?</td>\n",
+       "      <td>77053</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>82</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>132870</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>Exec-managerial</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>18</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>66</td>\n",
+       "      <td>?</td>\n",
+       "      <td>186061</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>54</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>140359</td>\n",
+       "      <td>7th-8th</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Machine-op-inspct</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age workclass  fnlwgt     education  education.num marital.status  \\\n",
+       "0   90         ?   77053       HS-grad              9        Widowed   \n",
+       "1   82   Private  132870       HS-grad              9        Widowed   \n",
+       "2   66         ?  186061  Some-college             10        Widowed   \n",
+       "3   54   Private  140359       7th-8th              4       Divorced   \n",
+       "\n",
+       "          occupation   relationship   race     sex  capital.gain  \\\n",
+       "0                  ?  Not-in-family  White  Female             0   \n",
+       "1    Exec-managerial  Not-in-family  White  Female             0   \n",
+       "2                  ?      Unmarried  Black  Female             0   \n",
+       "3  Machine-op-inspct      Unmarried  White  Female             0   \n",
+       "\n",
+       "   capital.loss  hours.per.week native.country  income  \n",
+       "0          4356              40  United-States       0  \n",
+       "1          4356              18  United-States       0  \n",
+       "2          4356              40  United-States       0  \n",
+       "3          3900              40  United-States       0  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Reformat Column We Are Predicting\n",
+    "dataset['income']=dataset['income'].map({'<=50K': 0, '>50K': 1, '<=50K.': 0, '>50K.': 1})\n",
+    "dataset.head(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "7ad8dd1d-470b-4e1e-b7b5-44769b775131",
+    "_execution_state": "idle",
+    "_uuid": "6cb8261a58c38c0492512efdbd1621d910965d52"
+   },
+   "source": [
+    "##3. Analyze Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "_cell_guid": "1c4744ab-7e9a-4993-ae0e-3c725912c638",
+    "_execution_state": "idle",
+    "_uuid": "31c7a7019b800cdea2eae4d2904b3ad428219da2"
+   },
+   "outputs": [],
+   "source": [
+    "# Identify Numeric features\n",
+    "numeric_features = ['age','fnlwgt','education.num','capital.gain','capital.loss','hours.per.week','income']\n",
+    "\n",
+    "# Identify Categorical features\n",
+    "cat_features = ['workclass','education','marital.status', 'occupation', 'relationship', 'race', 'sex', 'native']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "0ade92f2-fcb2-487d-a581-34a89f85e3c5",
+    "_execution_state": "idle",
+    "_uuid": "1d0cdf67c8b35f2389931629a5f105823a42c804"
+   },
+   "source": [
+    "###3.1. Numeric Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "_cell_guid": "7004fe8d-fefd-4ade-805a-1dddf9c88928",
+    "_execution_state": "idle",
+    "_uuid": "bd1dbe810e1435bf36a9e262a115382c6c2761f4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4b6508cb00>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Count of >50K & <=50K\n",
+    "sns.countplot(dataset['income'],label=\"Count\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "_cell_guid": "dc5bcab4-92fc-461c-9d7c-1a9580d23846",
+    "_execution_state": "idle",
+    "_uuid": "c92ee829ae5ca6724f3778a45f4e2a3df5f88c2a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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g37Z8nm3c79t/Q6PR0PezJ7k3faU53T/qYfa2vCjrJTuFfahfodfr9ZZO4nlF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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4b4b26fb38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Correlation matrix between numerical values\n",
+    "g = sns.heatmap(dataset[numeric_features].corr(),annot=True, fmt = \".2f\", cmap = \"coolwarm\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "_cell_guid": "57867c79-49b3-4e90-a196-aa9a297040a3",
+    "_execution_state": "idle",
+    "_uuid": "56537a5cce38758ab1e8514cbb7e12da27339984"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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z5GCd8e2PbVRAUNt6n/vKw1zWaKpGZ0iBgYGeMJKktm3bKigoyNSmAAD+p9EZUufOnTV7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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4b4ad66c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Explore Education Num vs Income\n",
+    "g = sns.factorplot(x=\"education.num\",y=\"income\",data=dataset,kind=\"bar\",size = 6,palette = \"muted\")\n",
+    "g.despine(left=True)\n",
+    "g = g.set_ylabels(\">50K probability\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "_cell_guid": "40c1d5aa-7f1c-4cdd-94c6-fd074d404162",
+    "_execution_state": "idle",
+    "_uuid": "7d13c85496761accb5791803b816e6152d5b4fc8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ltpDatm1LZmYm+/btY//+/eTm5tK+ffvyqE1ExGlcbT63a9EHgT8qNZDGjRvHe++9R1pa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\nNGDkyJF07drV/tx+/frRqVMnhgwZQtO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rJT4+ntHRUfr7+5k/fz6xsbFcvXoVp9NJamoq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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4b4acac978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Explore Hours Per Week vs Income\n",
+    "g  = sns.factorplot(x=\"hours.per.week\",y=\"income\",data=dataset,kind=\"bar\",size = 6,palette = \"muted\")\n",
+    "g.despine(left=True)\n",
+    "g = g.set_ylabels(\">50K probability\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "_cell_guid": "c719cef4-5d7f-4218-b5d5-f6ec2941a51e",
+    "_execution_state": "idle",
+    "_uuid": "2fa3fe2ff1f5dc96f2502767ce87263d86eb077b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kQhpsY0jfFx7sx02T4rG1K/z+zS85UVDj7JCEm6szt1JY3sDV8SFo1INj/KiT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\nwiVIQhJCCOESJCEJIYRwCf8fJpjjy321qM8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4b4750a5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Explore Age vs Income\n",
+    "g = sns.FacetGrid(dataset, col='income')\n",
+    "g = g.map(sns.distplot, \"age\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "cb5fb656-d724-4448-97b5-931560fcf9f2",
+    "_execution_state": "idle",
+    "_uuid": "076ad21bd294eb420d70df41407144cf12903e67"
+   },
+   "source": [
+    "###3.2. Categorical Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "_cell_guid": "dbccf607-7eb6-4e8b-8c00-2e91a2477ffe",
+    "_execution_state": "idle",
+    "_uuid": "0367e18fa3ea49392b03696aa5e75443c98ea471"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age               0\n",
+       "workclass         0\n",
+       "fnlwgt            0\n",
+       "education         0\n",
+       "education.num     0\n",
+       "marital.status    0\n",
+       "occupation        0\n",
+       "relationship      0\n",
+       "race              0\n",
+       "sex               0\n",
+       "capital.gain      0\n",
+       "capital.loss      0\n",
+       "hours.per.week    0\n",
+       "native.country    0\n",
+       "income            0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fill Missing Category Entries\n",
+    "dataset[\"workclass\"] = dataset[\"workclass\"].fillna(\"X\")\n",
+    "dataset[\"occupation\"] = dataset[\"occupation\"].fillna(\"X\")\n",
+    "dataset[\"native.country\"] = dataset[\"native.country\"].fillna(\"United-States\")\n",
+    "\n",
+    "# Confirm All Missing Data is Handled\n",
+    "dataset.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "_cell_guid": "5c6b1b5c-886d-44b1-ac09-1d410cda49d1",
+    "_execution_state": "idle",
+    "_uuid": "557e3c876124213d21e797095c23f6e0b9a5c5ab"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IOb9JkyY+LUxERETkiCoDy9q1a383zzAMPvjgA58UJCIiInKsKgPLhx9+6I86RERERCpV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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4b4af3d710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Explore Native Nation vs Income\n",
+    "g = sns.barplot(x=\"native.country\",y=\"income\",data=dataset)\n",
+    "g = g.set_ylabel(\"Income >50K Probability\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "_cell_guid": "30b367e2-afec-49fa-b52a-0371f1d6c0bc",
+    "_execution_state": "idle",
+    "_uuid": "36779048741bd7d8c4be010aab17761657dcc952"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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36QAAQLN5LPXBgwc3GI8cOdLnYQAAQOt5LPXf/va3kqTS0lKVl5fLMAxFRkbypDkAAC5S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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4b4ac3a5f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Explore Sex vs Income\n",
+    "g = sns.barplot(x=\"sex\",y=\"income\",data=dataset)\n",
+    "g = g.set_ylabel(\"Income >50K Probability\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "_cell_guid": "70d38dd5-6ea6-4f6b-a5ea-2cd8d7c5e3b4",
+    "_execution_state": "idle",
+    "_uuid": "2b3ef28f7ccd536387bb5dc29f30a63df5c1f8a8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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//34ZhqGQkBBmcAAAuIXTQNq+fbueeuoplZeXq0GDBrLZbDpw4IBq1aqlmTNnqm3btp6s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\nhf8Hsv2PiWchbIIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4b4aa63588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Explore Relationship vs Income\n",
+    "g = sns.factorplot(x=\"relationship\",y=\"income\",data=dataset,kind=\"bar\", size = 6 ,\n",
+    "palette = \"muted\")\n",
+    "g.despine(left=True)\n",
+    "g = g.set_ylabels(\"Income >50K Probability\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "_cell_guid": "c35908c8-7587-4dd6-826a-044a61a1fddd",
+    "_execution_state": "idle",
+    "_uuid": "8b0379c50391da248a88b6062e290200af00e65b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NUe3atQkPD2fr1q3m/bNmzXJ8dSIiUuYUGkzt2rUr0O7WrZvDixERESk0mP785z8DkJiY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\nKQomERGxFAWTiIhYyv8H/F4TJGZ00AAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4b47045780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Explore Marital Status vs Income\n",
+    "g = sns.factorplot(x=\"marital.status\",y=\"income\",data=dataset,kind=\"bar\", size = 6 ,\n",
+    "palette = \"muted\")\n",
+    "g.despine(left=True)\n",
+    "g = g.set_ylabels(\"Income >50K Probability\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "_cell_guid": "6dcfbb5a-dff6-4d90-9ed5-9c217810a7ea",
+    "_execution_state": "idle",
+    "_uuid": "5199bc6918c701af26e84802b068fc858f9d0617"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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s2JBPP/2UNWvWMGLECAWSiIhYVbFHSm+++aZlaKE6deoQHh5OfHy8ZfqMGTNK7DwsLIxd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\n2QAoAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4b46fc6a20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Explore Workclass vs Income\n",
+    "g = sns.factorplot(x=\"workclass\",y=\"income\",data=dataset,kind=\"bar\", size = 6 ,\n",
+    "palette = \"muted\")\n",
+    "g.despine(left=True)\n",
+    "g = g.set_ylabels(\"Income >50K Probability\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "f370f948-2810-48a9-b54f-b4c9c8ff851d",
+    "_execution_state": "idle",
+    "_uuid": "be23c9e7f4907134e69ca32b3e50de52e7f0a111"
+   },
+   "source": [
+    "##4. Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "_cell_guid": "1f2a9e2f-273b-495b-a615-4e9b8dd33c69",
+    "_execution_state": "idle",
+    "_uuid": "33f5e2439162592c594918029c6ececc4d6fa817"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset with Dropped Labels\n",
+      "   age  fnlwgt  education.num  marital.status  sex  capital.gain  \\\n",
+      "0   90   77053              9               0    1             0   \n",
+      "1   82  132870              9               0    1             0   \n",
+      "2   66  186061             10               0    1             0   \n",
+      "3   54  140359              4               0    1             0   \n",
+      "4   41  264663             10               0    1             0   \n",
+      "\n",
+      "   capital.loss  hours.per.week  income  \n",
+      "0          4356              40       0  \n",
+      "1          4356              18       0  \n",
+      "2          4356              40       0  \n",
+      "3          3900              40       0  \n",
+      "4          3900              40       0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "####################################################\n",
+    "############### FEATURE ENGINEERING ################\n",
+    "####################################################\n",
+    "# Convert Sex value to 0 and 1\n",
+    "dataset[\"sex\"] = dataset[\"sex\"].map({\"Male\": 0, \"Female\":1})\n",
+    "\n",
+    "# Create Married Column - Binary Yes(1) or No(0)\n",
+    "dataset[\"marital.status\"] = dataset[\"marital.status\"].replace(['Never-married','Divorced','Separated','Widowed'], 'Single')\n",
+    "dataset[\"marital.status\"] = dataset[\"marital.status\"].replace(['Married-civ-spouse','Married-spouse-absent','Married-AF-spouse'], 'Married')\n",
+    "dataset[\"marital.status\"] = dataset[\"marital.status\"].map({\"Married\":1, \"Single\":0})\n",
+    "dataset[\"marital.status\"] = dataset[\"marital.status\"].astype(int)\n",
+    "\n",
+    "# Drop the data you don't want to use\n",
+    "dataset.drop(labels=[\"workclass\",\"education\",\"occupation\",\"relationship\",\"race\",\"native.country\"], axis = 1, inplace = True)\n",
+    "print('Dataset with Dropped Labels')\n",
+    "print(dataset.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "46f46b2b-9ecc-4010-88c2-ae7872f80d8f",
+    "_execution_state": "idle",
+    "_uuid": "ab574da3a5eed4439cd48fe5dd6e681f01778d6e"
+   },
+   "source": [
+    "##5. Modeling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "_cell_guid": "2168869f-7cc2-4156-94a2-f1d27faaea17",
+    "_execution_state": "idle",
+    "_uuid": "a8b064a5427aa5e314094466a394d623bf7f020c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Split Data: X\n",
+      "[[    90  77053      9 ...,      0   4356     40]\n",
+      " [    82 132870      9 ...,      0   4356     18]\n",
+      " [    66 186061     10 ...,      0   4356     40]\n",
+      " ..., \n",
+      " [    40 154374      9 ...,      0      0     40]\n",
+      " [    58 151910      9 ...,      0      0     40]\n",
+      " [    22 201490      9 ...,      0      0     20]]\n",
+      "Split Data: Y\n",
+      "[0 0 0 ..., 1 0 0]\n",
+      "LR: 0.796529 (0.004215)\n",
+      "LDA: 0.829507 (0.004318)\n",
+      "KNN: 0.774455 (0.005765)\n",
+      "CART: 0.808623 (0.005509)\n",
+      "NB: 0.794303 (0.003642)\n",
+      "RF: 0.840641 (0.004920)\n"
+     ]
+    }
+   ],
+   "source": [
+    "###################################################\n",
+    "##################### MODELING #####################\n",
+    "####################################################\n",
+    "# Split-out Validation Dataset and Create Test Variables\n",
+    "array = dataset.values\n",
+    "X = array[:,0:8]\n",
+    "Y = array[:,8]\n",
+    "print('Split Data: X')\n",
+    "print(X)\n",
+    "print('Split Data: Y')\n",
+    "print(Y)\n",
+    "validation_size = 0.20\n",
+    "seed = 7\n",
+    "num_folds = 10\n",
+    "scoring = 'accuracy'\n",
+    "X_train, X_validation, Y_train, Y_validation = train_test_split(X,Y,\n",
+    "    test_size=validation_size,random_state=seed)\n",
+    "\n",
+    "# Params for Random Forest\n",
+    "num_trees = 100\n",
+    "max_features = 3\n",
+    "\n",
+    "#Spot Check 5 Algorithms (LR, LDA, KNN, CART, GNB, SVM)\n",
+    "models = []\n",
+    "models.append(('LR', LogisticRegression()))\n",
+    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
+    "models.append(('KNN', KNeighborsClassifier()))\n",
+    "models.append(('CART', DecisionTreeClassifier()))\n",
+    "models.append(('NB', GaussianNB()))\n",
+    "models.append(('RF', RandomForestClassifier(n_estimators=num_trees, max_features=max_features)))\n",
+    "#models.append(('SVM', SVC()))\n",
+    "# evalutate each model in turn\n",
+    "results = []\n",
+    "names = []\n",
+    "for name, model in models:\n",
+    "    kfold = KFold(n_splits=10, random_state=seed)\n",
+    "    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
+    "    results.append(cv_results)\n",
+    "    names.append(name)\n",
+    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
+    "    print(msg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "_cell_guid": "28db75e2-b091-4199-a358-e83b26e29836",
+    "_execution_state": "idle",
+    "_uuid": "8be8d86aeb3f97e762f7184db80c2fa4f5fcb5ed"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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sy9K0adO0atUq1atXT3FxcRo1apTuuece+fr6KjIyUh06dKiKugEAqLYs24CbyNLS0tSj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\nowAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4b4710ec50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "fig.suptitle('Algorith Comparison')\n",
+    "ax = fig.add_subplot(111)\n",
+    "plt.boxplot(results)\n",
+    "ax.set_xticklabels(names)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "bc5073e0-41df-4105-8215-ad8cb89241f8",
+    "_execution_state": "idle",
+    "_uuid": "a016da59293800e79278556bae8a0aa550e46184"
+   },
+   "source": [
+    "##6. Algorithm Tuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "_cell_guid": "ce7f4775-1714-482c-a6de-ceeae6fac0d2",
+    "_execution_state": "idle",
+    "_uuid": "6d3b6b7ac9a070d9c9513d56d40aa3e2a75ce997"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nCommented Out to Reduce Script Time - Took 20 Minutes to run.\\nbest n_estimator = 250\\nbest max_feature = 5\\n# Tune Random Forest\\nn_estimators = np.array([50,100,150,200,250])\\nmax_features = np.array([1,2,3,4,5])\\nparam_grid = dict(n_estimators=n_estimators,max_features=max_features)\\nmodel = RandomForestClassifier()\\nkfold = KFold(n_splits=num_folds, random_state=seed)\\ngrid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scoring, cv=kfold)\\ngrid_result = grid.fit(X_train, Y_train)\\nprint(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\\nmeans = grid_result.cv_results_[\\'mean_test_score\\']\\nstds = grid_result.cv_results_[\\'std_test_score\\']\\nparams = grid_result.cv_results_[\\'params\\']\\nfor mean, stdev, param in zip(means, stds, params):\\n    print(\"%f (%f) with: %r\" % (mean, stdev, param))\\n'"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "####################################################\n",
+    "################ ALGORITHM TUNING ##################\n",
+    "####################################################\n",
+    "'''\n",
+    "Commented Out to Reduce Script Time - Took 20 Minutes to run.\n",
+    "best n_estimator = 250\n",
+    "best max_feature = 5\n",
+    "# Tune Random Forest\n",
+    "n_estimators = np.array([50,100,150,200,250])\n",
+    "max_features = np.array([1,2,3,4,5])\n",
+    "param_grid = dict(n_estimators=n_estimators,max_features=max_features)\n",
+    "model = RandomForestClassifier()\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed)\n",
+    "grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scoring, cv=kfold)\n",
+    "grid_result = grid.fit(X_train, Y_train)\n",
+    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
+    "means = grid_result.cv_results_['mean_test_score']\n",
+    "stds = grid_result.cv_results_['std_test_score']\n",
+    "params = grid_result.cv_results_['params']\n",
+    "for mean, stdev, param in zip(means, stds, params):\n",
+    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "52cb2e35-1e66-4e7c-bc74-4e7e149d6a60",
+    "_execution_state": "idle",
+    "_uuid": "7491499e7b6de7b0b50cd671a4db9262062d0679"
+   },
+   "source": [
+    "##7. Finalize Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "_cell_guid": "a1c36e3d-3aaa-4952-854a-1d9093387928",
+    "_execution_state": "idle",
+    "_uuid": "6a62aa15b02a8d0de1166725e8122344418785ed"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 84.308306464%\n",
+      "[[4559  405]\n",
+      " [ 617  932]]\n",
+      "             precision    recall  f1-score   support\n",
+      "\n",
+      "          0       0.88      0.92      0.90      4964\n",
+      "          1       0.70      0.60      0.65      1549\n",
+      "\n",
+      "avg / total       0.84      0.84      0.84      6513\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "####################################################\n",
+    "################# FINALIZE MODEL ###################\n",
+    "####################################################\n",
+    "# 5. Finalize Model\n",
+    "# a) Predictions on validation dataset - KNN\n",
+    "random_forest = RandomForestClassifier(n_estimators=250,max_features=5)\n",
+    "random_forest.fit(X_train, Y_train)\n",
+    "predictions = random_forest.predict(X_validation)\n",
+    "print(\"Accuracy: %s%%\" % (100*accuracy_score(Y_validation, predictions)))\n",
+    "print(confusion_matrix(Y_validation, predictions))\n",
+    "print(classification_report(Y_validation, predictions))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}