Course_1_Final_Assignment.ipynb

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   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n",
    "\n",
    "<h1 align=\"center\"><font size=\"5\">Classification with Python</font></h1>"
   ]
  },
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   "source": [
    "In this notebook we try to practice all the classification algorithms that we 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",
    "Lets first load required libraries:"
   ]
  },
  {
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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"
   ]
  },
  {
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   "source": [
    "### About dataset"
   ]
  },
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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                                                               |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Lets download the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
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     "text": [
      "--2020-05-31 20:34:06--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.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: 23101 (23K) [text/csv]\n",
      "Saving to: ‘loan_train.csv’\n",
      "\n",
      "100%[======================================>] 23,101      --.-K/s   in 0.002s  \n",
      "\n",
      "2020-05-31 20:34:06 (11.6 MB/s) - ‘loan_train.csv’ saved [23101/23101]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O loan_train.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Load Data From CSV File  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</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>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: 0.1  Unnamed: 0 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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('loan_train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(346, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
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   "metadata": {
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   },
   "source": [
    "### Convert to date time object "
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</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>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",
       "      <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",
       "    </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",
       "    </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",
       "    </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: 0.1  Unnamed: 0 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": 4,
     "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",
    "\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 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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   },
   "source": [
    "260 people have paid off the loan on time while 86 have gone into collection \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot some columns to underestand data better:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: / ^C\n",
      "failed\n",
      "\n",
      "CondaError: KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# notice: installing seaborn might takes a few minutes\n",
    "!conda install -c anaconda seaborn -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ff/45fH190a9fP6xevRpbtmzB33//XbejICIiagKEEDhw4AD27duHwYMH48CBAygsLERgYCAAYPz48fjiiy/qvP+OHTvi8uXLAIDff/8dANCpU6ca161cp1JISAgsLCykqfI+KrntzZ1eLgIfPnwYjo6OaNWqFQYPHox3331XeoJrfHw8bGxs0KtXL2n9gIAAGBgYICEhodowptFooNFopPm8vDx9DJuIWgjWFKpvu3fvhoWFBUpLS1FRUYHnnnsOCxcuxAsvvICxY8dK9yCFhIRg7ty5SE9PR/v27WvdjxCiyg3bQgjZ23/44YcICAiQ5u9+Hcn92pu7Wn/SdD9BQUH46quvEBcXh/fffx9HjhxBcHAwysvLAdx+d5Cjo6PWNpV3/2dlZVW7z+joaFhbW0uTm5ubrodNRC0IawrVt0GDBiE5ORmpqakoKirCxo0bodFosH37dnzyySdQKpVQKpVo3bo1ysrK8OWXX9apn4sXL8LDwwMA0KFDB2lZTetWrlPJ2dkZnp6e0mRubl6r9uZO56Fp3LhxGDZsGHx8fDB8+HDs3r0bJ0+exOHDh+u8z6ioKOTm5krTlStXdDdgImpxWFOovpmbm8PT0xPu7u7Sp0qbNm3CQw89hDNnziA5OVmaVqxYgQ0bNkgfNsh18OBBnDt3DiNHjgQADBkyBLa2tlixYkWVdXft2oXU1FSEhIQ8+MG1IHr/jma7du1gb2+PtLQ0+Pv7w9nZWXoLcaWysjLk5OTUeEe+SqWCSqXS91CJqIVgTaHG4IsvvsCoUaPQuXNnreVubm6IiopCbGwsnnrqqWq31Wg0yMrK0nrkQHR0NJ5++mlMmDABwO2g9umnn2LcuHGYOnUqIiMjYWVlhbi4OMydOxejRo3CmDFj9H6czYneQ9Nff/2FmzdvStc9/fz8oFarkZSUhJ49ewK4nY4rKirg6+ur7+EQEVEzk1pW1uT6SUpKwpkzZ7B+/foqbdbW1vD398cXX3xRY2iKjY2Fi4sLlEolWrVqha5du2LVqlUICwvTerjlqFGjcOjQISxZsgT9+/dHcXExvLy8MH/+fMyYMYPv8qulWoem/Px8pKWlSfOXLl1CcnIybG1tYWtri0WLFmHkyJFwdnZGeno65s2bB09PT+mbAZ06dUJQUBCmTJmCdevWobS0FJGRkRg3bhxcXV11d2RERNSs2dvbw8zEBK+qc+qtTzMTE9jb29dqmw0bNlRZ1rNnz3veoL13794at9+wYUO1+6xJ//79tZ6VWJP73TBemxvKm6tah6bExEQMGjRImp81axYAICwsDGvXrsXZs2exceNGqNVquLq6YsiQIXjnnXe0PgrftGkTIiMj4e/vLz3cctWqVTo4HCIiainc3d1xMSWF756jelPr0DRw4MB7ps19+/bddx+2trbYvHlzbbsmIiLS4u7uzhBD9Ubn354jIiIiao4YmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSQe+vUSEiItKXjIwMPtyS6g1DExERNUkZGRnw7tgJxUWF9daniakZUn67WOvglJWVhSVLlmDPnj24evUqHB0d0a1bN8yYMQP+/v4AgGPHjuHdd99FfHw8ioqK4OXlhYkTJ2L69OkwNDSU9qVQKLB9+3YMHz68Sj+HDx/GoEGD8M8//8DGxqZK+8KFC7Fo0aIqy729vfHbb79J82lpaViyZAn279+PGzduwNXVFX369MHs2bNx/vx5TJw48Z7He+nSJWzYsAE7duxAcnKytDwnJweLFy/G9u3bkZmZCXt7ewQFBWHhwoVa5zQ8PBwbN25EdHQ0Xn/9dWn5jh07MGLEiAZ7pQtDExERNUnZ2dkoLipE+xHzYGrvpvf+irKvIH37MmRnZ9cqNF2+fBl9+/aFjY0Nli9fDh8fH5SWlmLfvn2IiIjAb7/9hu3bt2PMmDGYOHEiDh06BBsbGxw4cADz5s1DfHw8tm7dqrOX6z7yyCM4cOCA1jKl8n9xIDExEf7+/ujcuTM+/fRTdOzYEbdu3cLOnTsxe/ZsxMbGIigoSFr/2WefRefOnbF48WJpmYODQ5V+c3Jy0KdPHxgbG2PdunV45JFHcPnyZbz55pt49NFHER8fj3bt2knrm5iY4P3338eLL76IVq1a6eTYHxRDExERNWmm9m4wd/Fq6GHU6JVXXoFCocCJEydgbm4uLX/kkUcwadIkFBQUYMqUKRg2bBg+++wzqf2FF16Ak5MThg0bhq1bt2Ls2LE6GY9SqYSzs3O1bUIIhIeHw8vLCz///DMMDP5363O3bt0wffp0mJqawtTUVFpubGwMMzOzGvdZaf78+fj777+RlpYmrevu7o59+/bBy8sLERER+PHHH6X1AwICkJaWhujoaCxbtuxBDllneCM4ERGRnuTk5CA2NhYRERFagamSjY0NfvrpJ9y8eRNz5syp0j506FB06NAB3377bX0MF8nJybhw4QJmz56tFZgqVXfJT46Kigps2bIFoaGhVcKVqakpXnnlFezbtw85OTnSckNDQ7z33ntYvXo1/vrrrzr1q2sMTURERHqSlpYGIQQ6duxY4zq///47AKBTp07Vtnfs2FFaRxfOnTsHCwsLremll14CAKSmpkp96tKNGzegVqtrPMZOnTpBCIG0tDSt5SNGjEC3bt3w9ttv63Q8dcXLc0RERHpSmxuW6+vmZm9vb+zatUtrmZWVVb2MoS77f//99zF48OBqP4mrb/ykiYiISE+8vLygUCi0vpl2tw4dOgAALl68WG37xYsXpXV0wdjYGJ6enlqTo6Oj1ljuNd66cHBwgI2NzT2PUaFQwNPTs0rbgAEDEBgYiKioKJ2OqS4YmoiIiPTE1tYWgYGBWLNmDQoKCqq0q9VqDBkyBLa2tlixYkWV9l27diE1NRUhISH1MVx069YNDz/8MFasWIGKiooq7Wq1uk77NTAwwJgxY7B582ZkZWVptRUVFeGTTz5BYGAgbG1tq91+6dKl+OGHHxAfH1+n/nWFoYmIiEiP1qxZg/LycvTu3Rv/+c9/kJqaiosXL2LVqlXw8/ODubk5Pv30U+zcuRNTp07F2bNncfnyZXzxxRcIDw/HqFGjMGbMGK19Xrp0CcnJyVrTnaHs3LlzWm1nzpyR2srKypCVlaU1Xbt2DcDtZ0DFxMTg999/R//+/bF371788ccfOHv2LJYsWYJnnnmmzufhvffeg7OzM5544gn8+OOPuHLlCo4ePYrAwECUlpZizZo1NW7r4+OD0NBQrFq1qs796wLvaSIioiatKPtKo+6nXbt2OHXqFJYsWYLZs2cjMzMTDg4O6NmzJ9auXQsAGDVqFA4dOoQlS5agf//+KC4uhpeXF+bPn48ZM2ZUeUbTrFmzqvTz888/S/8eMGCAVpuhoSHKysoAABcuXICLi4tWu0qlQnFxMQCgd+/eSExMxJIlSzBlyhRkZ2fDxcUFjz32GFauXFmncwAAdnZ2OH78OBYvXowXX3wRWVlZsLW1RXBwML755pv7Pvtq8eLF+Pe//13n/nVBIRrqsZoPIC8vD9bW1sjNzZVuXiOiB3Pq1Cn07NkTP9o7wsfYWC99nCspQXD2dSQlJaFHjx566aMuWFMav+LiYly6dAkeHh4wMTEB0LSeCE71o7qfE13iJ01ERNQkubu7I+W3i3z3HNWbWoemo0ePYvny5UhKSkJmZqbW+29KS0vx5ptvStdAra2tERAQgKVLl8LV1VXaR9u2bfHnn39q7ffu98sQERHdj7u7O0MM1Zta3wheUFCArl27VnvDVmFhIU6dOoUFCxbg1KlT2LZtG1JSUjBs2LAq6y5evBiZmZnSNG3atLodAREREVE9qPUnTcHBwQgODq62zdraGvv379da9vHHH6N3797IyMjQ+mvA0tLyvu+pISIiImos9P7IgdzcXCgUiirvq1m6dCns7OzQvXt3LF++XLqrvzoajQZ5eXlaExFRXbGmEFFd6PVG8OLiYrz22msICQnR+kbKq6++ih49esDW1hbHjh1DVFQUMjMz8cEHH1S7n+joaCxatEifQyWiFoQ1pemq7oGLRJX0/fPxQI8cUCgUWjeC36m0tBQjR47EX3/9hcOHD9/za7xffvklXnzxReTn50OlUlVp12g00Gg00nxeXh7c3Nz49WAiHWpJjxxgTWl6KioqkJqaCkNDQzg4OMDY2LjKs4uo5RJCoKSkBDdu3EB5eTm8vLxgYKD7i2l6+aSptLQUY8aMwZ9//omDBw/etwj5+vqirKwMly9fhre3d5V2lUpVbZgiIqoL1pSmx8DAAB4eHsjMzMTff//d0MOhRsrMzAzu7u56CUyAHkJTZWBKTU3FoUOHYGdnd99tkpOTYWBgIL0wkIiI6G7GxsZwd3dHWVkZysvLG3o41MgYGhpCqVTq9RPIWoem/Px8pKWlSfOV77+xtbWFi4sLRo0ahVOnTmH37t0oLy+XXsxna2sLY2NjxMfHIyEhAYMGDYKlpSXi4+Mxc+ZMjB8/Hq1atdLdkRERUbOjUChgZGQEIyOjhh4KtUC1Dk2JiYkYNGiQNF/5/puwsDAsXLgQu3btAnD7Tcl3OnToEAYOHAiVSoUtW7Zg4cKF0Gg08PDwwMyZM6t9jw4RERFRY1Hr0DRw4EDc697x+91X3qNHDxw/fry23RIRERE1KL0/p4mIiIioOWBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGWodmo4ePYqhQ4fC1dUVCoUCO3bs0GoXQuCtt96Ci4sLTE1NERAQgNTUVK11cnJyEBoaCisrK9jY2GDy5MnIz89/oAMhIiIi0qdah6aCggJ07doVa9asqbZ92bJlWLVqFdatW4eEhASYm5sjMDAQxcXF0jqhoaG4cOEC9u/fj927d+Po0aOYOnVq3Y+CiIiISM+Utd0gODgYwcHB1bYJIbBy5Uq8+eabeOaZZwAAX331FZycnLBjxw6MGzcOFy9eRGxsLE6ePIlevXoBAFavXo0nn3wS//rXv+Dq6voAh0NERESkHzq9p+nSpUvIyspCQECAtMza2hq+vr6Ij48HAMTHx8PGxkYKTAAQEBAAAwMDJCQk6HI4RERERDpT60+a7iUrKwsA4OTkpLXcyclJasvKyoKjo6P2IJRK2NraSuvcTaPRQKPRSPN5eXm6HDYRtTCsKURUF03i23PR0dGwtraWJjc3t4YeEhE1YawpRFQXOg1Nzs7OAIBr165pLb927ZrU5uzsjOvXr2u1l5WVIScnR1rnblFRUcjNzZWmK1eu6HLYRNTCsKYQUV3oNDR5eHjA2dkZcXFx0rK8vDwkJCTAz88PAODn5we1Wo2kpCRpnYMHD6KiogK+vr7V7lelUsHKykprIiKqK9YUIqqLWt/TlJ+fj7S0NGn+0qVLSE5Ohq2tLdzd3TFjxgy8++678PLygoeHBxYsWABXV1cMHz4cANCpUycEBQVhypQpWLduHUpLSxEZGYlx48bxm3NERETUaNU6NCUmJmLQoEHS/KxZswAAYWFh2LBhA+bNm4eCggJMnToVarUa/fr1Q2xsLExMTKRtNm3ahMjISPj7+8PAwAAjR47EqlWrdHA4RERERPpR69A0cOBACCFqbFcoFFi8eDEWL15c4zq2trbYvHlzbbsmIiIiajBN4ttzRERERA2NoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGTQeWhq27YtFApFlSkiIgIAMHDgwCptL730kq6HQURERKRTSl3v8OTJkygvL5fmz58/jyeeeAKjR4+Wlk2ZMgWLFy+W5s3MzHQ9DCIiIiKd0nlocnBw0JpfunQp2rdvj8cff1xaZmZmBmdnZ113TURERKQ3er2nqaSkBN988w0mTZoEhUIhLd+0aRPs7e3RuXNnREVFobCwUJ/DICIiInpgOv+k6U47duyAWq1GeHi4tOy5555DmzZt4OrqirNnz+K1115DSkoKtm3bVuN+NBoNNBqNNJ+Xl6fPYRNRM8eaQkR1odfQ9MUXXyA4OBiurq7SsqlTp0r/9vHxgYuLC/z9/ZGeno727dtXu5/o6GgsWrRIn0MlohaENYWI6kJvl+f+/PNPHDhwAC+88MI91/P19QUApKWl1bhOVFQUcnNzpenKlSs6HSsRtSysKURUF3r7pCkmJgaOjo546qmn7rlecnIyAMDFxaXGdVQqFVQqlS6HR0QtGGsKEdWFXkJTRUUFYmJiEBYWBqXyf12kp6dj8+bNePLJJ2FnZ4ezZ89i5syZGDBgALp06aKPoRARERHphF5C04EDB5CRkYFJkyZpLTc2NsaBAwewcuVKFBQUwM3NDSNHjsSbb76pj2EQERER6YxeQtOQIUMghKiy3M3NDUeOHNFHl0RERER6xXfPEREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJoLcX9hJR05RWVtok901EpG8MTUQEAMjMzAQUCkxT/6PfjhSK230RETUxDE1EBABQq9WAEGg/Yh5M7d300kdR9hWkb192uy8ioiaGoYmItJjau8Hcxauhh0FE1OjwRnAiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBp2HpoULF0KhUGhNHTt2lNqLi4sREREBOzs7WFhYYOTIkbh27Zquh0FERESkU3p5IvgjjzyCAwcO/K8T5f+6mTlzJvbs2YPvvvsO1tbWiIyMxLPPPotff/1VH0Mh0ruMjAxkZ2frtQ97e3u4u7vrtQ8iIro3vYQmpVIJZ2fnKstzc3PxxRdfYPPmzRg8eDAAICYmBp06dcLx48fRp08ffQyHSG8yMjLQydsbhcXFeu3HzMQEF1NSGJyIiBqQXkJTamoqXF1dYWJiAj8/P0RHR8Pd3R1JSUkoLS1FQECAtG7Hjh3h7u6O+Pj4GkOTRqOBRqOR5vPy8vQxbKJay87ORmFxMVbZ2MJLqZ9XOaaWleFVdQ6ys7MZmnSENYWI6kLnVd7X1xcbNmyAt7c3MjMzsWjRIvTv3x/nz59HVlYWjI2NYWNjo7WNk5MTsrKyatxndHQ0Fi1apOuhEumMl1IJH2Pjhh4GycSaQkR1ofMbwYODgzF69Gh06dIFgYGB2Lt3L9RqNbZu3VrnfUZFRSE3N1early5osMRE1FLw5pCRHWhn+sJd7CxsUGHDh2QlpaGJ554AiUlJVCr1VqfNl27dq3ae6AqqVQqqFQqfQ+ViFoI1hQiqgu9P6cpPz8f6enpcHFxQc+ePWFkZIS4uDipPSUlBRkZGfDz89P3UIiIiIjqTOefNM2ZMwdDhw5FmzZt8Pfff+Ptt9+GoaEhQkJCYG1tjcmTJ2PWrFmwtbWFlZUVpk2bBj8/P35zjoiIiBo1nYemv/76CyEhIbh58yYcHBzQr18/HD9+HA4ODgCADz/8EAYGBhg5ciQ0Gg0CAwPxySef6HoYRERERDql89C0ZcuWe7abmJhgzZo1WLNmja67JiIiItIbvnuOiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZFA29ACIiIio+cnIyEB2drbe+7G3t4e7u7ve+wEYmoiIiEjHMjIy0MnbG4XFxXrvy8zEBBdTUuolODE0ERERkU5lZ2ejsLgYq2xs4aXUX9RILSvDq+ocZGdnMzQRERFR0+WlVMLH2Lihh6EzvBGciIiISAadh6bo6Gg8+uijsLS0hKOjI4YPH46UlBStdQYOHAiFQqE1vfTSS7oeChEREZHO6Dw0HTlyBBERETh+/Dj279+P0tJSDBkyBAUFBVrrTZkyBZmZmdK0bNkyXQ+FiIiISGd0fk9TbGys1vyGDRvg6OiIpKQkDBgwQFpuZmYGZ2dnXXdPREREpBd6v6cpNzcXAGBra6u1fNOmTbC3t0fnzp0RFRWFwsLCGveh0WiQl5enNRER1RVrChHVhV6/PVdRUYEZM2agb9++6Ny5s7T8ueeeQ5s2beDq6oqzZ8/itddeQ0pKCrZt21btfqKjo7Fo0SJ9DpWIWhDWFCKqC72GpoiICJw/fx6//PKL1vKpU6dK//bx8YGLiwv8/f2Rnp6O9u3bV9lPVFQUZs2aJc3n5eXBzc1NfwMnomaNNYWI6kJvoSkyMhK7d+/G0aNH8dBDD91zXV9fXwBAWlpataFJpVJBpVLpZZxE1PKwphBRXeg8NAkhMG3aNGzfvh2HDx+Gh4fHfbdJTk4GALi4uOh6OEREREQ6ofPQFBERgc2bN2Pnzp2wtLREVlYWAMDa2hqmpqZIT0/H5s2b8eSTT8LOzg5nz57FzJkzMWDAAHTp0kXXwyEiIiLSCZ2HprVr1wK4/QDLO8XExCA8PBzGxsY4cOAAVq5ciYKCAri5uWHkyJF48803dT0UIiIiIp3Ry+W5e3Fzc8ORI0d03S0RERGRXvHdc0REREQyMDQRERERyaDX5zQRtRRpZaVNct9ERCQfQxPRA8jMzAQUCkxT/6PfjhSK230REVGDYWgiegBqtRoQAu1HzIOpvX6eKF2UfQXp25fd7ouIiBoMQxORDpjau8Hcxauhh0FERHrEG8GJiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhl4IzgREVEjkpGRgezsbL32YW9vD3d3d7320RwxNBERETUSGRkZ6OTtjcLiYr32Y2ZigospKQxOtcTQRERE1EhkZ2ejsLgYq2xs4aXUz/+iU8vK8Ko6B9nZ2QxNtcTQRERE1Mh4KZXwMTZu6GHQXXgjOBEREZEMDE1EREREMvDyHBEREelFWllpk97/3RiaiIiISKcyMzMBhQLT1P/ovzOF4nZ/9YChiYiIiHRKrVYDQqD9iHkwtXfTWz9F2VeQvn3Z7f7qAUMTERER6YWpvRvMXbwaehg602Chac2aNVi+fDmysrLQtWtXrF69Gr1799ZLX/XxdFWAT1glIiJqzhokNP373//GrFmzsG7dOvj6+mLlypUIDAxESkoKHB0dddpXfT1dFeATVomIiJqzBglNH3zwAaZMmYKJEycCANatW4c9e/bgyy+/xOuvv67Tvurj6apA/T1hle8karkuXbqEU6dO6XX/1PKwpjRO+vxWWOW+L168qLc+mms9qffQVFJSgqSkJERFRUnLDAwMEBAQgPj4eL312xyersp3ErVsCxYswIIFCxp6GNSMsKY0PvX2rTOFAuPHj9dvH81QvYem7OxslJeXw8nJSWu5k5MTfvvtt2q30Wg00Gg00nxubi4AIC8v77795efnAwB+LCrCudKSug77vv4qKwcAbNu2DUlJSXrp488//0RhcTFGmZjCztBQL33cLC/H98VF+Oyzz9CmTRu99FHJ0NAQ5eXlTbqPhIQEAEBBZhrKS4r00kfxzasAAH9jFTyNjPTSBwCc0mhwsqy0Xo6lsLBQ1u8vAFhaWkKhUOh0HA9SUwDg7Nmzev0rvZK+f36bU02pj3pSH/0kJCQAQsDZbxRUVvZ66UOTl42s+O/1WlPqo54ADVBTRD27evWqACCOHTumtXzu3Lmid+/e1W7z9ttvCwCcOHFqgVNubq7O6xBrCidOLXd6kJqiEEII1KOSkhKYmZnh+++/x/Dhw6XlYWFhUKvV2LlzZ5Vt7v6rsKKiAjk5ObCzs9P5X6D6kJeXBzc3N1y5cgVWVlYNPZwGwXPAcwDU7RzUxydNrClND88BzwFQ/zWl3i/PGRsbo2fPnoiLi5NCU0VFBeLi4hAZGVntNiqVCiqVSmuZjY2Nnkeqe1ZWVi32B7sSzwHPAdDw54A1pfngOeA5AOrvHDTIt+dmzZqFsLAw9OrVC71798bKlStRUFAgfZuOiIiIqLFpkNA0duxY3LhxA2+99RaysrLQrVs3xMbGVrk5nIiIiKixaLAngkdGRtZ4Oa65UalUePvtt6tcDmhJeA54DgCeA13heeQ5AHgOgPo/B/V+IzgRERFRU2TQ0AMgIiIiagoYmoiIiIhkYGgiIiIikoGhSUeWLl0KhUKBGTNmSMuKi4sREREBOzs7WFhYYOTIkbh27ZrWdhkZGXjqqadgZmYGR0dHzJ07F2VlZfU8+rq7evUqxo8fDzs7O5iamsLHxweJiYlSuxACb731FlxcXGBqaoqAgACkpqZq7SMnJwehoaGwsrKCjY0NJk+eLL3+prErLy/HggUL4OHhAVNTU7Rv3x7vvPMO7rxVsLmdg6NHj2Lo0KFwdXWFQqHAjh07tNp1dbxnz55F//79YWJiAjc3Nyxbtkzfh9ZotNR6ArCmsKY08prygG8jICHEiRMnRNu2bUWXLl3E9OnTpeUvvfSScHNzE3FxcSIxMVH06dNHPPbYY1J7WVmZ6Ny5swgICBCnT58We/fuFfb29iIqKqoBjqL2cnJyRJs2bUR4eLhISEgQf/zxh9i3b59IS0uT1lm6dKmwtrYWO3bsEGfOnBHDhg0THh4eoqioSFonKChIdO3aVRw/flz8/PPPwtPTU4SEhDTEIdXakiVLhJ2dndi9e7e4dOmS+O6774SFhYX46KOPpHWa2znYu3evmD9/vti2bZsAILZv367Vrovjzc3NFU5OTiI0NFScP39efPvtt8LU1FR8+umn9XWYDaal1hMhWFOEYE1p7DWFoekB3bp1S3h5eYn9+/eLxx9/XCpyarVaGBkZie+++05a9+LFiwKAiI+PF0Lc/kExMDAQWVlZ0jpr164VVlZWQqPR1Otx1MVrr70m+vXrV2N7RUWFcHZ2FsuXL5eWqdVqoVKpxLfffiuEEOK///2vACBOnjwprfPjjz8KhUIhrl69qr/B68hTTz0lJk2apLXs2WefFaGhoUKI5n8O7i5wujreTz75RLRq1Urr9+C1114T3t7eej6ihtWS64kQrClCsKY09prCy3MPKCIiAk899RQCAgK0liclJaG0tFRreceOHeHu7o74+HgAQHx8PHx8fLQe6hkYGIi8vDxcuHChfg7gAezatQu9evXC6NGj4ejoiO7du2P9+vVS+6VLl5CVlaV1DqytreHr66t1DmxsbNCrVy9pnYCAABgYGNx+23cj99hjjyEuLg6///47AODMmTP45ZdfEBwcDKBlnIM76ep44+PjMWDAABgbG0vrBAYGIiUlBf/88089HU39a8n1BGBNAVhT7tbYakqDPdyyOdiyZQtOnTqFkydPVmnLysqCsbFxlfdZOTk5ISsrS1rn7qegV85XrtOY/fHHH1i7di1mzZqFN954AydPnsSrr74KY2NjhIWFScdQ3THeeQ4cHR212pVKJWxtbZvEOXj99deRl5eHjh07wtDQEOXl5ViyZAlCQ0MBoEWcgzvp6nizsrLg4eFRZR+Vba1atdLL+BtSS68nAGsKwJpyt8ZWUxia6ujKlSuYPn069u/fDxMTk4YeToOoqKhAr1698N577wEAunfvjvPnz2PdunUICwtr4NHVj61bt2LTpk3YvHkzHnnkESQnJ2PGjBlwdXVtMeeAHhzryW2sKawpjR0vz9VRUlISrl+/jh49ekCpVEKpVOLIkSNYtWoVlEolnJycUFJSArVarbXdtWvX4OzsDABwdnau8u2XyvnKdRozFxcXPPzww1rLOnXqhIyMDAD/O4bqjvHOc3D9+nWt9rKyMuTk5DSJczB37ly8/vrrGDduHHx8fPD8889j5syZiI6OBtAyzsGddHW8Tf13o7ZYT25jTWFNuVtjqykMTXXk7++Pc+fOITk5WZp69eqF0NBQ6d9GRkaIi4uTtklJSUFGRgb8/PwAAH5+fjh37pzWf+z9+/fDysqqSuFojPr27YuUlBStZb///jvatGkDAPDw8ICzs7PWOcjLy0NCQoLWOVCr1UhKSpLWOXjwICoqKuDr61sPR/FgCgsLYWCg/WtkaGiIiooKAC3jHNxJV8fr5+eHo0ePorS0VFpn//798Pb2bpaX5lhPbmNNYU25W6OrKXW5u52qd+e3XYS4/RVhd3d3cfDgQZGYmCj8/PyEn5+f1F75FeEhQ4aI5ORkERsbKxwcHJrMV4RPnDghlEqlWLJkiUhNTRWbNm0SZmZm4ptvvpHWWbp0qbCxsRE7d+4UZ8+eFc8880y1XxXt3r27SEhIEL/88ovw8vJqtF+NvVtYWJho3bq19PXgbdu2CXt7ezFv3jxpneZ2Dm7duiVOnz4tTp8+LQCIDz74QJw+fVr8+eefQgjdHK9arRZOTk7i+eefF+fPnxdbtmwRZmZmLeKRA5VaWj0RgjVFCNaUxl5TGJp06O4iV1RUJF555RXRqlUrYWZmJkaMGCEyMzO1trl8+bIIDg4Wpqamwt7eXsyePVuUlpbW88jr7ocffhCdO3cWKpVKdOzYUXz22Wda7RUVFWLBggXCyclJqFQq4e/vL1JSUrTWuXnzpggJCREWFhbCyspKTJw4Udy6das+D6PO8vLyxPTp04W7u7swMTER7dq1E/Pnz9f6WmtzOweHDh0SAKpMYWFhQgjdHe+ZM2dEv379hEqlEq1btxZLly6tr0NsFFpiPRGCNYU1pXHXFIUQdzxmlIiIiIiqxXuaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmqhetW3bFitXrtTZ/sLDwzF8+HCd7Q8ADh8+DIVCUeXlqETU+LCmUH1SNvQAqOkKDw/Hxo0bAQBGRkZwd3fHhAkT8MYbb0CprP5H6+TJkzA3N9fZGD766CPwofZEzQNrCjV2DE30QIKCghATEwONRoO9e/ciIiICRkZGiIqK0lqvpKQExsbGcHBw0Gn/1tbWOt0fETUs1hRqzHh5jh6ISqWCs7Mz2rRpg5dffhkBAQHYtWuX9BH3kiVL4OrqCm9vbwBVP0pXKBT4/PPPMWLECJiZmcHLywu7du3S6uPChQt4+umnYWVlBUtLS/Tv3x/p6ekAqn6UPnDgQERGRiIyMhLW1tawt7fHggULtP5y/Prrr9GrVy9YWlrC2dkZzz33HK5fv66/k0REsrGmUGPG0EQ6ZWpqipKSEgBAXFwcUlJSsH//fuzevbvGbRYtWoQxY8bg7NmzePLJJxEaGoqcnBwAwNWrVzFgwACoVCocPHgQSUlJmDRpEsrKymrc38aNG6FUKnHixAl89NFH+OCDD/D5559L7aWlpXjnnXdw5swZ7NixA5cvX0Z4eLhuTgAR6RRrCjUmvDxHOiGEQFxcHPbt24dp06bhxo0bMDc3x+effw5jY+N7bhseHo6QkBAAwHvvvYdVq1bhxIkTCAoKwpo1a2BtbY0tW7bAyMgIANChQ4d77s/NzQ0ffvghFAoFvL29ce7cOXz44YeYMmUKAGDSpEnSuu3atcOqVavw6KOPIj8/HxYWFg9yGohIR1hTqDHiJ030QHbv3g0LCwuYmJggODgYY8eOxcKFCwEAPj4+9y1uANClSxfp3+bm5rCyspI+2k5OTkb//v2l4iZHnz59oFAopHk/Pz+kpqaivLwcAJCUlIShQ4fC3d0dlpaWePzxxwEAGRkZsvsgIv1gTaHGjJ800QMZNGgQ1q5dC2NjY7i6ump9w0XuN1ruLl4KhQIVFRUAbn80r0sFBQUIDAxEYGAgNm3aBAcHB2RkZCAwMFC6BEBEDYc1hRozhiZ6IObm5vD09NTb/rt06YKNGzeitLRU9l+GCQkJWvPHjx+Hl5cXDA0N8dtvv+HmzZtYunQp3NzcAACJiYk6HzcR1Q1rCjVmvDxHjVpkZCTy8vIwbtw4JCYmIjU1FV9//TVSUlJq3CYjIwOzZs1CSkoKvv32W6xevRrTp08HALi7u8PY2BirV6/GH3/8gV27duGdd96pr8MhogbGmkIPgqGJGjU7OzscPHgQ+fn5ePzxx9GzZ0+sX7/+nn8hTpgwAUVFRejduzciIiIwffp0TJ06FQDg4OCADRs24LvvvsPDDz+MpUuX4l//+ld9HQ4RNTDWFHoQCsFHn1IzMnDgQHTr1k2nr1UgopaLNYXuxE+aiIiIiGRgaCIiIiKSgZfniIiIiGTgJ01EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDL8H42wLzGrzbk6AAAAAElFTkSuQmCC\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": 7,
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Lets look at the day of the week people get the loan "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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 dont pay it off, so lets use Feature binarization to set a threshold values less then day 4 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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: 0.1</th>\n",
       "      <th>Unnamed: 0</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: 0.1  Unnamed: 0 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": 9,
     "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Lets look at gender:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "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": [
    "Lets convert male to 0 and female to 1:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</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: 0.1  Unnamed: 0 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": 11,
     "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",
    "#### How about education?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "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": [
    "#### Feature befor One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
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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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>High School or Below</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>Bechalor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>college</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>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": 13,
     "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 "
   ]
  },
  {
   "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",
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       "\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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       "  </thead>\n",
       "  <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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       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>15</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",
       "    </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": 14,
     "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Lets defind feature sets, X:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1000</td>\n",
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       "      <td>27</td>\n",
       "      <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",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <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": 15,
     "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?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['loan_status'].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Normalize Data "
   ]
  },
  {
   "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 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "Y= preprocessing.LabelEncoder().fit(y).transform(y)\n",
    "Y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Classification "
   ]
  },
  {
   "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",
    "- K Nearest Neighbor(KNN)\n",
    "- Decision Tree\n",
    "- Support Vector Machine\n",
    "- Logistic Regression\n",
    "\n",
    "\n",
    "\n",
    "__ Notice:__ \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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Nearest Neighbor(KNN)\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__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X (346, 8)\n",
      "The shape of Y (346,)\n",
      "The shape of X_train (276, 8)\n",
      "The shape of Y_train (276,)\n",
      "The shape of X_test (70, 8)\n",
      "The shape of Y_test (70,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "print(\"The shape of X\",X.shape)\n",
    "print(\"The shape of Y\",y.shape)\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.2, random_state=4)\n",
    "\n",
    "print(\"The shape of X_train\",X_train.shape)\n",
    "print(\"The shape of Y_train\",Y_train.shape)\n",
    "print(\"The shape of X_test\",X_test.shape)\n",
    "print(\"The shape of Y_test\",Y_test.shape)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best k for the dataset is  51  with cross validation acuuracy :  0.7545378151260504\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "no_k=100\n",
    "bestScore=0.0\n",
    "accu=[]\n",
    "for k in range(1,no_k):\n",
    "    kclass=KNeighborsClassifier(n_neighbors=k,algorithm='auto')\n",
    "    \n",
    "    scores=cross_val_score(kclass, X, Y, cv=10)\n",
    "    score=scores.mean()\n",
    "    accu.append(score)\n",
    "    \n",
    "    if score > bestScore:\n",
    "        bestScore=score\n",
    "        best_clf=kclass\n",
    "        bestK=k\n",
    "print(\"The best k for the dataset is \",bestK,\" with cross validation acuuracy : \",bestScore)\n",
    "kclass=best_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,no_k),accu)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('CrossValidation Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "kclass=KNeighborsClassifier(n_neighbors=bestK).fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7857142857142857"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(Y_test,kclass.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=20)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=20)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=20)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision=DecisionTreeClassifier(criterion=\"entropy\", max_depth =20)\n",
    "decision.fit(X_train,Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6857142857142857"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(Y_test,decision.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "support=svm.SVC(kernel=\"rbf\")\n",
    "support.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7428571428571429"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(Y_test,support.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LogisticRegression(C=0.1,solver='liblinear').fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7428571428571429"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(Y_test,lr.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation using Test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, download and load the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-05-31 20:57:47--  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",
      "100%[======================================>] 3,642       --.-K/s   in 0s      \n",
      "\n",
      "2020-05-31 20:57:47 (152 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 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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: 0.1</th>\n",
       "      <th>Unnamed: 0</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: 0.1  Unnamed: 0 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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('loan_test.csv')\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54, 10)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</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>2016-09-08</td>\n",
       "      <td>2016-10-07</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>2016-09-09</td>\n",
       "      <td>2016-09-15</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>2016-09-10</td>\n",
       "      <td>2016-10-09</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>2016-09-10</td>\n",
       "      <td>2016-10-09</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>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>29</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0.1  Unnamed: 0 loan_status  Principal  terms effective_date  \\\n",
       "0             1           1     PAIDOFF       1000     30     2016-09-08   \n",
       "1             5           5     PAIDOFF        300      7     2016-09-09   \n",
       "2            21          21     PAIDOFF       1000     30     2016-09-10   \n",
       "3            24          24     PAIDOFF       1000     30     2016-09-10   \n",
       "4            35          35     PAIDOFF        800     15     2016-09-11   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0 2016-10-07   50              Bechalor  female  \n",
       "1 2016-09-15   35       Master or Above    male  \n",
       "2 2016-10-09   43  High School or Below  female  \n",
       "3 2016-10-09   26               college    male  \n",
       "4 2016-09-25   29              Bechalor    male  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAIDOFF       40\n",
       "COLLECTION    14\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <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>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
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       "      <td>5</td>\n",
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       "    <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>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "      <td>5</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>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>29</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>male</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0.1  Unnamed: 0 loan_status  Principal  terms effective_date  \\\n",
       "0             1           1     PAIDOFF       1000     30     2016-09-08   \n",
       "1             5           5     PAIDOFF        300      7     2016-09-09   \n",
       "2            21          21     PAIDOFF       1000     30     2016-09-10   \n",
       "3            24          24     PAIDOFF       1000     30     2016-09-10   \n",
       "4            35          35     PAIDOFF        800     15     2016-09-11   \n",
       "\n",
       "    due_date  age             education  Gender  dayofweek  \n",
       "0 2016-10-07   50              Bechalor  female          3  \n",
       "1 2016-09-15   35       Master or Above    male          4  \n",
       "2 2016-10-09   43  High School or Below  female          5  \n",
       "3 2016-10-09   26               college    male          5  \n",
       "4 2016-09-25   29              Bechalor    male          6  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['dayofweek']=test_df['effective_date'].dt.dayofweek\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2016-10-09</td>\n",
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       "      <td>0</td>\n",
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       "      <td>24</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
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       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <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>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>29</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>male</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0.1  Unnamed: 0 loan_status  Principal  terms effective_date  \\\n",
       "0             1           1     PAIDOFF       1000     30     2016-09-08   \n",
       "1             5           5     PAIDOFF        300      7     2016-09-09   \n",
       "2            21          21     PAIDOFF       1000     30     2016-09-10   \n",
       "3            24          24     PAIDOFF       1000     30     2016-09-10   \n",
       "4            35          35     PAIDOFF        800     15     2016-09-11   \n",
       "\n",
       "    due_date  age             education  Gender  dayofweek  weekend  \n",
       "0 2016-10-07   50              Bechalor  female          3        0  \n",
       "1 2016-09-15   35       Master or Above    male          4        0  \n",
       "2 2016-10-09   43  High School or Below  female          5        0  \n",
       "3 2016-10-09   26               college    male          5        1  \n",
       "4 2016-09-25   29              Bechalor    male          6        1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['weekend']=df['dayofweek'].apply(lambda x:1 if x>3 else 0)  \n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "      <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>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</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>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>29</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0.1  Unnamed: 0 loan_status  Principal  terms effective_date  \\\n",
       "0             1           1     PAIDOFF       1000     30     2016-09-08   \n",
       "1             5           5     PAIDOFF        300      7     2016-09-09   \n",
       "2            21          21     PAIDOFF       1000     30     2016-09-10   \n",
       "3            24          24     PAIDOFF       1000     30     2016-09-10   \n",
       "4            35          35     PAIDOFF        800     15     2016-09-11   \n",
       "\n",
       "    due_date  age             education  Gender  dayofweek  weekend  \n",
       "0 2016-10-07   50              Bechalor       1          3        0  \n",
       "1 2016-09-15   35       Master or Above       0          4        0  \n",
       "2 2016-10-09   43  High School or Below       1          5        0  \n",
       "3 2016-10-09   26               college       0          5        1  \n",
       "4 2016-09-25   29              Bechalor       0          6        1  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Gender'].replace(to_replace=['male','female'],value=[0,1],inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Principal</th>\n",
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       "      <td>1</td>\n",
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       "      <td>college</td>\n",
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       "      <th>4</th>\n",
       "      <td>800</td>\n",
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       "      <td>0</td>\n",
       "      <td>Bechalor</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Principal  terms  age  Gender             education\n",
       "0       1000     30   50       1              Bechalor\n",
       "1        300      7   35       0       Master or Above\n",
       "2       1000     30   43       1  High School or Below\n",
       "3       1000     30   26       0               college\n",
       "4        800     15   29       0              Bechalor"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df[['Principal','terms','age','Gender','education']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>0</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>800</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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   50       1        0         1                     0   \n",
       "1        300      7   35       0        0         0                     0   \n",
       "2       1000     30   43       1        0         0                     1   \n",
       "3       1000     30   26       0        1         0                     0   \n",
       "4        800     15   29       0        1         1                     0   \n",
       "\n",
       "   college  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        1  \n",
       "4        0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NewFeature=test_df[['Principal','terms','age','Gender','weekend']]\n",
    "NewFeature=pd.concat([NewFeature,pd.get_dummies(test_df['education'])],axis=1)\n",
    "NewFeature.drop(['Master or Above'], axis = 1,inplace=True)\n",
    "NewFeature.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xnew = NewFeature\n",
    "Xnew= preprocessing.StandardScaler().fit_transform(Xnew)\n",
    "Ynew = pd.get_dummies(test_df['loan_status'])['PAIDOFF'].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of test data is  (54, 8)\n",
      "The shape of ground truth data is (54,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"The shape of test data is \",Xnew.shape)\n",
    "print(\"The shape of ground truth data is\",Ynew.shape)\n",
    "Ynew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import jaccard_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import log_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Algor=['KNN','Decision Tree','SVM','Logistic Regression']\n",
    "jacc=[]\n",
    "f1=[]\n",
    "log=[]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "n1=kclass.predict(Xnew)\n",
    "jacc.append(jaccard_score(Ynew,n1))\n",
    "f1.append(f1_score(Ynew,n1))\n",
    "log.append('NA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "n2=decision.predict(Xnew)\n",
    "jacc.append(jaccard_score(Ynew,n2))\n",
    "f1.append(f1_score(Ynew,n2))\n",
    "log.append('NA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "n3=support.predict(Xnew)\n",
    "jacc.append(jaccard_score(Ynew,n3))\n",
    "f1.append(f1_score(Ynew,n3))\n",
    "log.append('NA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "n4=lr.predict(Xnew)\n",
    "probability=lr.predict_proba(Xnew)\n",
    "jacc.append(jaccard_score(Ynew,n4))\n",
    "f1.append(f1_score(Ynew,n4))\n",
    "log.append(log_loss(Ynew,probability))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Report\n",
    "You should be able to report the accuracy of the built model using different evaluation metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Jaccard</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>LogLoss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.851064</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.489362</td>\n",
       "      <td>0.657143</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.795918</td>\n",
       "      <td>0.886364</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.851064</td>\n",
       "      <td>0.547395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Algorithm   Jaccard  F1-score   LogLoss\n",
       "0                  KNN  0.740741  0.851064        NA\n",
       "1        Decision Tree  0.489362  0.657143        NA\n",
       "2                  SVM  0.795918  0.886364        NA\n",
       "3  Logistic Regression  0.740741  0.851064  0.547395"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score=pd.DataFrame({'Algorithm':Algor,'Jaccard':jacc,'F1-score':f1,'LogLoss':log})\n",
    "score"
   ]
  },
  {
   "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\">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\">Watson Studio</a>\n",
    "\n",
    "<h3>Thanks for completing this lesson!</h3>\n",
    "\n",
    "<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">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",
    "<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>"
   ]
  }
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