ML0101EN-Proj-Loan-py-v1.ipynb

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   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n",
    "\n",
    "<h1 align=\"center\"><font size=\"5\">Classification with Python</font></h1>\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "In this notebook we try to practice all the classification algorithms that we have learned in this course.\n",
    "\n",
    "We load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.\n",
    "\n",
    "Let's first load required libraries:\n"
   ]
  },
  {
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   "execution_count": 3,
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   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import NullFormatter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.ticker as ticker\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#notice: Disable all warnings \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "### About dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "This dataset is about past loans. The **Loan_train.csv** data set includes details of 346 customers whose loan are already paid off or defaulted. It includes following fields:\n",
    "\n",
    "| Field          | Description                                                                           |\n",
    "| -------------- | ------------------------------------------------------------------------------------- |\n",
    "| Loan_status    | Whether a loan is paid off on in collection                                           |\n",
    "| Principal      | Basic principal loan amount at the                                                    |\n",
    "| Terms          | Origination terms which can be weekly (7 days), biweekly, and monthly payoff schedule |\n",
    "| Effective_date | When the loan got originated and took effects                                         |\n",
    "| Due_date       | Since it’s one-time payoff schedule, each loan has one single due date                |\n",
    "| Age            | Age of applicant                                                                      |\n",
    "| Education      | Education of applicant                                                                |\n",
    "| Gender         | The gender of applicant                                                               |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "Let's download the dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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     "text": [
      "--2022-09-24 00:40:53--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/loan_train.csv\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23101 (23K) [text/csv]\n",
      "Saving to: ‘loan_train.csv’\n",
      "\n",
      "loan_train.csv      100%[===================>]  22.56K  --.-KB/s    in 0s      \n",
      "\n",
      "2022-09-24 00:40:53 (53.1 MB/s) - ‘loan_train.csv’ saved [23101/23101]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O loan_train.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/loan_train.csv"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Load Data From CSV File\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>10/7/2016</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>10/7/2016</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>9/22/2016</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>10/8/2016</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>10/8/2016</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30       9/8/2016   \n",
       "1           2             2     PAIDOFF       1000     30       9/8/2016   \n",
       "2           3             3     PAIDOFF       1000     15       9/8/2016   \n",
       "3           4             4     PAIDOFF       1000     30       9/9/2016   \n",
       "4           6             6     PAIDOFF       1000     30       9/9/2016   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0  10/7/2016   45  High School or Below    male  \n",
       "1  10/7/2016   33              Bechalor  female  \n",
       "2  9/22/2016   27               college    male  \n",
       "3  10/8/2016   28               college  female  \n",
       "4  10/8/2016   29               college    male  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('loan_train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(346, 10)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
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   "metadata": {
    "button": false,
    "new_sheet": false,
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   },
   "source": [
    "### Convert to date time object\n"
   ]
  },
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   "execution_count": 8,
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
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       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
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       "      <td>college</td>\n",
       "      <td>male</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0 2016-10-07   45  High School or Below    male  \n",
       "1 2016-10-07   33              Bechalor  female  \n",
       "2 2016-09-22   27               college    male  \n",
       "3 2016-10-08   28               college  female  \n",
       "4 2016-10-08   29               college    male  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['due_date'] = pd.to_datetime(df['due_date'])\n",
    "df['effective_date'] = pd.to_datetime(df['effective_date'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Data visualization and pre-processing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let’s see how many of each class is in our data set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAIDOFF       260\n",
       "COLLECTION     86\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "260 people have paid off the loan on time while 86 have gone into collection\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot some columns to underestand data better:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (0.9.0)\n",
      "Requirement already satisfied: scipy>=0.14.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from seaborn) (1.7.3)\n",
      "Requirement already satisfied: pandas>=0.15.2 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from seaborn) (1.3.5)\n",
      "Requirement already satisfied: matplotlib>=1.4.3 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from seaborn) (3.5.3)\n",
      "Requirement already satisfied: numpy>=1.9.3 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from seaborn) (1.21.6)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (2.8.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (0.11.0)\n",
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      "Requirement already satisfied: pillow>=6.2.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (8.1.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (1.4.4)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (4.37.3)\n",
      "Requirement already satisfied: pytz>=2017.3 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from pandas>=0.15.2->seaborn) (2022.2.1)\n",
      "Requirement already satisfied: typing-extensions in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.3->seaborn) (4.3.0)\n",
      "Requirement already satisfied: six>=1.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib>=1.4.3->seaborn) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "# notice: installing seaborn might takes a few minutes\n",
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "bins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'Principal', bins=bins, ec=\"k\")\n",
    "\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(df.age.min(), df.age.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'age', bins=bins, ec=\"k\")\n",
    "\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Pre-processing:  Feature selection/extraction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Let's look at the day of the week people get the loan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['dayofweek'] = df['effective_date'].dt.dayofweek\n",
    "bins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'dayofweek', bins=bins, ec=\"k\")\n",
    "g.axes[-1].legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We see that people who get the loan at the end of the week don't pay it off, so let's use Feature binarization to set a threshold value less than day 4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  dayofweek  weekend  \n",
       "0 2016-10-07   45  High School or Below    male          3        0  \n",
       "1 2016-10-07   33              Bechalor  female          3        0  \n",
       "2 2016-09-22   27               college    male          3        0  \n",
       "3 2016-10-08   28               college  female          4        1  \n",
       "4 2016-10-08   29               college    male          4        1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Convert Categorical features to numerical values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's look at gender:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender  loan_status\n",
       "female  PAIDOFF        0.865385\n",
       "        COLLECTION     0.134615\n",
       "male    PAIDOFF        0.731293\n",
       "        COLLECTION     0.268707\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "86 % of female pay there loans while only 73 % of males pay there loan\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's convert male to 0 and female to 1:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>dayofweek</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  dayofweek  weekend  \n",
       "0 2016-10-07   45  High School or Below       0          3        0  \n",
       "1 2016-10-07   33              Bechalor       1          3        0  \n",
       "2 2016-09-22   27               college       0          3        0  \n",
       "3 2016-10-08   28               college       1          4        1  \n",
       "4 2016-10-08   29               college       0          4        1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## One Hot Encoding\n",
    "\n",
    "#### How about education?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education             loan_status\n",
       "Bechalor              PAIDOFF        0.750000\n",
       "                      COLLECTION     0.250000\n",
       "High School or Below  PAIDOFF        0.741722\n",
       "                      COLLECTION     0.258278\n",
       "Master or Above       COLLECTION     0.500000\n",
       "                      PAIDOFF        0.500000\n",
       "college               PAIDOFF        0.765101\n",
       "                      COLLECTION     0.234899\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['education'])['loan_status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Features before One Hot Encoding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>High School or Below</td>\n",
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       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>Bechalor</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
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       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>college</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
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       "      <td>1</td>\n",
       "      <td>college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>college</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender             education\n",
       "0       1000     30   45       0  High School or Below\n",
       "1       1000     30   33       1              Bechalor\n",
       "2       1000     15   27       0               college\n",
       "3       1000     30   28       1               college\n",
       "4       1000     30   29       0               college"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Principal','terms','age','Gender','education']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Bechalor</th>\n",
       "      <th>High School or Below</th>\n",
       "      <th>college</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
       "0       1000     30   45       0        0         0                     1   \n",
       "1       1000     30   33       1        0         1                     0   \n",
       "2       1000     15   27       0        0         0                     0   \n",
       "3       1000     30   28       1        1         0                     0   \n",
       "4       1000     30   29       0        1         0                     0   \n",
       "\n",
       "   college  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Feature = df[['Principal','terms','age','Gender','weekend']]\n",
    "Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\n",
    "Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
    "Feature.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Feature Selection\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's define feature sets, X:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Bechalor</th>\n",
       "      <th>High School or Below</th>\n",
       "      <th>college</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
       "0       1000     30   45       0        0         0                     1   \n",
       "1       1000     30   33       1        0         1                     0   \n",
       "2       1000     15   27       0        0         0                     0   \n",
       "3       1000     30   28       1        1         0                     0   \n",
       "4       1000     30   29       0        1         0                     0   \n",
       "\n",
       "   college  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Feature\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "What are our lables?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "y=[0]*len(df)\n",
    "for index, loan_status in df.iterrows():\n",
    "    y[index] = 1 if loan_status['loan_status'] == 'PAIDOFF' else 0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Normalize Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Data Standardization give data zero mean and unit variance (technically should be done after train test split)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.51578458,  0.92071769,  2.33152555, -0.42056004, -1.20577805,\n",
       "        -0.38170062,  1.13639374, -0.86968108],\n",
       "       [ 0.51578458,  0.92071769,  0.34170148,  2.37778177, -1.20577805,\n",
       "         2.61985426, -0.87997669, -0.86968108],\n",
       "       [ 0.51578458, -0.95911111, -0.65321055, -0.42056004, -1.20577805,\n",
       "        -0.38170062, -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.48739188,  2.37778177,  0.82934003,\n",
       "        -0.38170062, -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.3215732 , -0.42056004,  0.82934003,\n",
       "        -0.38170062, -0.87997669,  1.14984679]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# df_test = pd.read_csv('./loan_test.csv')\n",
    "# df_test['due_date'] = pd.to_datetime(df_test['due_date'])\n",
    "# df_test['effective_date'] = pd.to_datetime(df_test['effective_date'])\n",
    "# df_test['weekend'] = df_test['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
    "# df_test['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
    "# test_Feature = df_test[['Principal','terms','age','Gender','weekend']]\n",
    "# test_Feature = pd.concat([test_Feature,pd.get_dummies(df_test['education'])], axis=1)\n",
    "# test_Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
    "# test_y=[0]*len(df)\n",
    "# for index, loan_status in df_test.iterrows():\n",
    "#     test_y[index] = 1 if loan_status['loan_status'] == 'PAIDOFF' else 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "tags": []
   },
   "source": [
    "# Classification\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now, it is your turn, use the training set to build an accurate model. Then use the test set to report the accuracy of the model\n",
    "You should use the following algorithm:\n",
    "\n",
    "*   K Nearest Neighbor(KNN)\n",
    "*   Decision Tree\n",
    "*   Support Vector Machine\n",
    "*   Logistic Regression\n",
    "\n",
    "\\__ Notice:\\__\n",
    "\n",
    "*   You can go above and change the pre-processing, feature selection, feature-extraction, and so on, to make a better model.\n",
    "*   You should use either scikit-learn, Scipy or Numpy libraries for developing the classification algorithms.\n",
    "*   You should include the code of the algorithm in the following cells.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Nearest Neighbor(KNN)\n",
    "\n",
    "Notice: You should find the best k to build the model with the best accuracy.\\\n",
    "**warning:** You should not use the **loan_test.csv** for finding the best k, however, you can split your train_loan.csv into train and test to find the best **k**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,y,test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7571428571428571\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train,Y_train)\n",
    "yhat_knn = knn.predict(X_test)\n",
    "print(accuracy_score(Y_test,yhat_knn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6857142857142857\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train,Y_train)\n",
    "yhat_dt = dt.predict(X_test)\n",
    "print(accuracy_score(Y_test,yhat_dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm = SVC()\n",
    "svm.fit(X_train,Y_train)\n",
    "yhat_svm = svm.predict(X_test)\n",
    "print(accuracy_score(Y_test,yhat_svm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train,Y_train)\n",
    "yhat_lr = lr.predict(X_test)\n",
    "print(accuracy_score(Y_test,yhat_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation using Test set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn==0.20.4 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (0.20.4)\n",
      "Requirement already satisfied: numpy>=1.8.2 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from scikit-learn==0.20.4) (1.21.6)\n",
      "Requirement already satisfied: scipy>=0.13.3 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from scikit-learn==0.20.4) (1.7.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn==0.20.4\n",
    "from sklearn.metrics import jaccard_similarity_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import log_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, download and load the test set:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-09-24 00:48:21--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3642 (3.6K) [text/csv]\n",
      "Saving to: ‘loan_test.csv’\n",
      "\n",
      "loan_test.csv       100%[===================>]   3.56K  --.-KB/s    in 0s      \n",
      "\n",
      "2022-09-24 00:48:21 (22.4 MB/s) - ‘loan_test.csv’ saved [3642/3642]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Load Test set for evaluation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>10/7/2016</td>\n",
       "      <td>50</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>300</td>\n",
       "      <td>7</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>9/15/2016</td>\n",
       "      <td>35</td>\n",
       "      <td>Master or Above</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/10/2016</td>\n",
       "      <td>10/9/2016</td>\n",
       "      <td>43</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/10/2016</td>\n",
       "      <td>10/9/2016</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>9/11/2016</td>\n",
       "      <td>9/25/2016</td>\n",
       "      <td>29</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           1             1     PAIDOFF       1000     30       9/8/2016   \n",
       "1           5             5     PAIDOFF        300      7       9/9/2016   \n",
       "2          21            21     PAIDOFF       1000     30      9/10/2016   \n",
       "3          24            24     PAIDOFF       1000     30      9/10/2016   \n",
       "4          35            35     PAIDOFF        800     15      9/11/2016   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0  10/7/2016   50              Bechalor  female  \n",
       "1  9/15/2016   35       Master or Above    male  \n",
       "2  10/9/2016   43  High School or Below  female  \n",
       "3  10/9/2016   26               college    male  \n",
       "4  9/25/2016   29              Bechalor    male  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('loan_test.csv')\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['due_date'] = pd.to_datetime(test_df['due_date'])\n",
    "test_df['effective_date'] = pd.to_datetime(test_df['effective_date'])\n",
    "test_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\n",
    "test_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
    "test_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
    "test_Feature = test_df[['Principal','terms','age','Gender','weekend']]\n",
    "test_Feature = pd.concat([test_Feature,pd.get_dummies(test_df['education'])], axis=1)\n",
    "test_Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
    "test_X = test_Feature\n",
    "test_X= preprocessing.StandardScaler().fit(test_X).transform(test_X)\n",
    "test_y=[0]*len(test_df)\n",
    "for index, loan_status in test_df.iterrows():\n",
    "    test_y[index] = 1 if loan_status['loan_status'] == 'PAIDOFF' else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_yhat_knn = knn.predict(test_X)\n",
    "test_yhat_dt = dt.predict(test_X)\n",
    "test_yhat_svm = svm.predict(test_X)\n",
    "test_yhat_lr = lr.predict(test_X)\n",
    "# print(jaccard_score(test_y, test_yhat_knn, average = None), f1_score(test_y, test_yhat_knn), log_loss(test_y, test_yhat_knn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7222222222222222 0.8192771084337349\n",
      "0.7777777777777778 0.8461538461538461\n",
      "0.7037037037037037 0.8222222222222222\n",
      "0.7962962962962963 0.8791208791208792 7.035839553995365\n"
     ]
    }
   ],
   "source": [
    "print(jaccard_similarity_score(test_y, test_yhat_knn), f1_score(test_y, test_yhat_knn))\n",
    "print(jaccard_similarity_score(test_y, test_yhat_dt), f1_score(test_y, test_yhat_dt))\n",
    "print(jaccard_similarity_score(test_y, test_yhat_svm), f1_score(test_y, test_yhat_svm))\n",
    "print(jaccard_similarity_score(test_y, test_yhat_lr), f1_score(test_y, test_yhat_lr), log_loss(test_y, test_yhat_lr))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Report\n",
    "\n",
    "You should be able to report the accuracy of the built model using different evaluation metrics:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Algorithm          | Jaccard | F1-score | LogLoss |\n",
    "| ------------------ | ------- | -------- | ------- |\n",
    "| KNN                | 0.72    | 0.82     | NA      |\n",
    "| Decision Tree      | 0.78    | 0.85     | NA      |\n",
    "| SVM                | 0.70    | 0.82     | NA      |\n",
    "| LogisticRegression | 0.79    | 0.88     | 7.04    |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2>Want to learn more?</h2>\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">SPSS Modeler</a>\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Watson Studio</a>\n",
    "\n",
    "<h3>Thanks for completing this lesson!</h3>\n",
    "\n",
    "<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Saeed Aghabozorgi</a></h4>\n",
    "<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
    "\n",
    "<hr>\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description                                                             |\n",
    "| ----------------- | ------- | ------------- | ------------------------------------------------------------------------------ |\n",
    "| 2020-10-27        | 2.1     | Lakshmi Holla | Made changes in import statement due to updates in version of  sklearn library |\n",
    "| 2020-08-27        | 2.0     | Malika Singla | Added lab to GitLab                                                            |\n",
    "\n",
    "<hr>\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
    "\n",
    "<p>\n"
   ]
  }
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