MLAlgorithms (1).ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align: center;\">\n",
    "Basic Data Analysis & Machine Learning Techniques\n",
    "</h1>\n",
    "<p style=\"text-align: center;\">\n",
    "A few basic machine learning techniques: with data analysis and visualization methods\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing all the libraries\n",
    "\n",
    "This step is important to import all the libraries required for our project. Some of them are in-built while some need to be installed from third-party websites. In case of any error in this step, go through the official documentation to understand how the installation works for your system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statistics import mean\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import tree\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Dataset\n",
    "\n",
    "This step is important to load the data from the .xls, .csv files into our algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
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       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default payment next month</th>\n",
       "    </tr>\n",
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       "      <th>ID</th>\n",
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       "      <td>3261</td>\n",
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       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
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       "      <td>1</td>\n",
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       "      <td>90000</td>\n",
       "      <td>2</td>\n",
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       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
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       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
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       "      <td>5000</td>\n",
       "      <td>0</td>\n",
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       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
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       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>88004</td>\n",
       "      <td>31237</td>\n",
       "      <td>15980</td>\n",
       "      <td>8500</td>\n",
       "      <td>20000</td>\n",
       "      <td>5003</td>\n",
       "      <td>3047</td>\n",
       "      <td>5000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>29997</th>\n",
       "      <td>150000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8979</td>\n",
       "      <td>5190</td>\n",
       "      <td>0</td>\n",
       "      <td>1837</td>\n",
       "      <td>3526</td>\n",
       "      <td>8998</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20878</td>\n",
       "      <td>20582</td>\n",
       "      <td>19357</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22000</td>\n",
       "      <td>4200</td>\n",
       "      <td>2000</td>\n",
       "      <td>3100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29999</th>\n",
       "      <td>80000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>52774</td>\n",
       "      <td>11855</td>\n",
       "      <td>48944</td>\n",
       "      <td>85900</td>\n",
       "      <td>3409</td>\n",
       "      <td>1178</td>\n",
       "      <td>1926</td>\n",
       "      <td>52964</td>\n",
       "      <td>1804</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>30000</th>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>36535</td>\n",
       "      <td>32428</td>\n",
       "      <td>15313</td>\n",
       "      <td>2078</td>\n",
       "      <td>1800</td>\n",
       "      <td>1430</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
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       "<p>30000 rows × 24 columns</p>\n",
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      ],
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       "       LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "ID                                                                            \n",
       "1          20000    2          2         1   24      2      2     -1     -1   \n",
       "2         120000    2          2         2   26     -1      2      0      0   \n",
       "3          90000    2          2         2   34      0      0      0      0   \n",
       "4          50000    2          2         1   37      0      0      0      0   \n",
       "5          50000    1          2         1   57     -1      0     -1      0   \n",
       "...          ...  ...        ...       ...  ...    ...    ...    ...    ...   \n",
       "29996     220000    1          3         1   39      0      0      0      0   \n",
       "29997     150000    1          3         2   43     -1     -1     -1     -1   \n",
       "29998      30000    1          2         2   37      4      3      2     -1   \n",
       "29999      80000    1          3         1   41      1     -1      0      0   \n",
       "30000      50000    1          2         1   46      0      0      0      0   \n",
       "\n",
       "       PAY_5  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "ID            ...                                                        \n",
       "1         -2  ...          0          0          0         0       689   \n",
       "2          0  ...       3272       3455       3261         0      1000   \n",
       "3          0  ...      14331      14948      15549      1518      1500   \n",
       "4          0  ...      28314      28959      29547      2000      2019   \n",
       "5          0  ...      20940      19146      19131      2000     36681   \n",
       "...      ...  ...        ...        ...        ...       ...       ...   \n",
       "29996      0  ...      88004      31237      15980      8500     20000   \n",
       "29997      0  ...       8979       5190          0      1837      3526   \n",
       "29998      0  ...      20878      20582      19357         0         0   \n",
       "29999      0  ...      52774      11855      48944     85900      3409   \n",
       "30000      0  ...      36535      32428      15313      2078      1800   \n",
       "\n",
       "       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "ID                                                                         \n",
       "1             0         0         0         0                           1  \n",
       "2          1000      1000         0      2000                           1  \n",
       "3          1000      1000      1000      5000                           0  \n",
       "4          1200      1100      1069      1000                           0  \n",
       "5         10000      9000       689       679                           0  \n",
       "...         ...       ...       ...       ...                         ...  \n",
       "29996      5003      3047      5000      1000                           0  \n",
       "29997      8998       129         0         0                           0  \n",
       "29998     22000      4200      2000      3100                           1  \n",
       "29999      1178      1926     52964      1804                           1  \n",
       "30000      1430      1000      1000      1000                           1  \n",
       "\n",
       "[30000 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are so many ways to load the dataset and this is one method that works easily\n",
    "#Note that the command varies for .xls and .csv files\n",
    "#Clean (remove/add certain rows or columns or data) your data based on what you require form the input file\n",
    "\n",
    "df = pd.read_excel(\"defaultpay.xls\", index_col=0, skiprows=1)\n",
    "#Removed the first row since it is not required\n",
    "#Assigned the first column to be our index column since we already have index in our input file\n",
    "#Do refer the actual input .xls file to see the changes we made\n",
    "\n",
    "df                                                       #Outputs the data after cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "This step is important to show the basic info related to the data. There are different ways to explore data based on our requirement. Below are a few of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "ID                                                                         \n",
       "1       20000    2          2         1   24      2      2     -1     -1   \n",
       "2      120000    2          2         2   26     -1      2      0      0   \n",
       "3       90000    2          2         2   34      0      0      0      0   \n",
       "4       50000    2          2         1   37      0      0      0      0   \n",
       "5       50000    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "    PAY_5  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "ID         ...                                                                  \n",
       "1      -2  ...          0          0          0         0       689         0   \n",
       "2       0  ...       3272       3455       3261         0      1000      1000   \n",
       "3       0  ...      14331      14948      15549      1518      1500      1000   \n",
       "4       0  ...      28314      28959      29547      2000      2019      1200   \n",
       "5       0  ...      20940      19146      19131      2000     36681     10000   \n",
       "\n",
       "    PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "ID                                                            \n",
       "1          0         0         0                           1  \n",
       "2       1000         0      2000                           1  \n",
       "3       1000      1000      5000                           0  \n",
       "4       1100      1069      1000                           0  \n",
       "5       9000       689       679                           0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()                                                #Outputs the first 5 rows of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default payment next month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29996</th>\n",
       "      <td>220000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>88004</td>\n",
       "      <td>31237</td>\n",
       "      <td>15980</td>\n",
       "      <td>8500</td>\n",
       "      <td>20000</td>\n",
       "      <td>5003</td>\n",
       "      <td>3047</td>\n",
       "      <td>5000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29997</th>\n",
       "      <td>150000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8979</td>\n",
       "      <td>5190</td>\n",
       "      <td>0</td>\n",
       "      <td>1837</td>\n",
       "      <td>3526</td>\n",
       "      <td>8998</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29998</th>\n",
       "      <td>30000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20878</td>\n",
       "      <td>20582</td>\n",
       "      <td>19357</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22000</td>\n",
       "      <td>4200</td>\n",
       "      <td>2000</td>\n",
       "      <td>3100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29999</th>\n",
       "      <td>80000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>52774</td>\n",
       "      <td>11855</td>\n",
       "      <td>48944</td>\n",
       "      <td>85900</td>\n",
       "      <td>3409</td>\n",
       "      <td>1178</td>\n",
       "      <td>1926</td>\n",
       "      <td>52964</td>\n",
       "      <td>1804</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30000</th>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>36535</td>\n",
       "      <td>32428</td>\n",
       "      <td>15313</td>\n",
       "      <td>2078</td>\n",
       "      <td>1800</td>\n",
       "      <td>1430</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "ID                                                                            \n",
       "29996     220000    1          3         1   39      0      0      0      0   \n",
       "29997     150000    1          3         2   43     -1     -1     -1     -1   \n",
       "29998      30000    1          2         2   37      4      3      2     -1   \n",
       "29999      80000    1          3         1   41      1     -1      0      0   \n",
       "30000      50000    1          2         1   46      0      0      0      0   \n",
       "\n",
       "       PAY_5  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "ID            ...                                                        \n",
       "29996      0  ...      88004      31237      15980      8500     20000   \n",
       "29997      0  ...       8979       5190          0      1837      3526   \n",
       "29998      0  ...      20878      20582      19357         0         0   \n",
       "29999      0  ...      52774      11855      48944     85900      3409   \n",
       "30000      0  ...      36535      32428      15313      2078      1800   \n",
       "\n",
       "       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "ID                                                                         \n",
       "29996      5003      3047      5000      1000                           0  \n",
       "29997      8998       129         0         0                           0  \n",
       "29998     22000      4200      2000      3100                           1  \n",
       "29999      1178      1926     52964      1804                           1  \n",
       "30000      1430      1000      1000      1000                           1  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()                                                 #Outputs the last 5 rows of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30000 entries, 1 to 30000\n",
      "Data columns (total 24 columns):\n",
      " #   Column                      Non-Null Count  Dtype\n",
      "---  ------                      --------------  -----\n",
      " 0   LIMIT_BAL                   30000 non-null  int64\n",
      " 1   SEX                         30000 non-null  int64\n",
      " 2   EDUCATION                   30000 non-null  int64\n",
      " 3   MARRIAGE                    30000 non-null  int64\n",
      " 4   AGE                         30000 non-null  int64\n",
      " 5   PAY_0                       30000 non-null  int64\n",
      " 6   PAY_2                       30000 non-null  int64\n",
      " 7   PAY_3                       30000 non-null  int64\n",
      " 8   PAY_4                       30000 non-null  int64\n",
      " 9   PAY_5                       30000 non-null  int64\n",
      " 10  PAY_6                       30000 non-null  int64\n",
      " 11  BILL_AMT1                   30000 non-null  int64\n",
      " 12  BILL_AMT2                   30000 non-null  int64\n",
      " 13  BILL_AMT3                   30000 non-null  int64\n",
      " 14  BILL_AMT4                   30000 non-null  int64\n",
      " 15  BILL_AMT5                   30000 non-null  int64\n",
      " 16  BILL_AMT6                   30000 non-null  int64\n",
      " 17  PAY_AMT1                    30000 non-null  int64\n",
      " 18  PAY_AMT2                    30000 non-null  int64\n",
      " 19  PAY_AMT3                    30000 non-null  int64\n",
      " 20  PAY_AMT4                    30000 non-null  int64\n",
      " 21  PAY_AMT5                    30000 non-null  int64\n",
      " 22  PAY_AMT6                    30000 non-null  int64\n",
      " 23  default payment next month  30000 non-null  int64\n",
      "dtypes: int64(24)\n",
      "memory usage: 5.7 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()                                                 #Outputs some info of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2',\n",
       "       'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
       "       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n",
       "       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6',\n",
       "       'default payment next month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns                                                #Outputs all the columns in our data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling and Data Visualization\n",
    "\n",
    "This step is important to verify our data and fill in any missing values (that is Data Wrangling). Data Visualization is visual representation of our data to make our data easy to interpret. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIMIT_BAL                     0\n",
       "SEX                           0\n",
       "EDUCATION                     0\n",
       "MARRIAGE                      0\n",
       "AGE                           0\n",
       "PAY_0                         0\n",
       "PAY_2                         0\n",
       "PAY_3                         0\n",
       "PAY_4                         0\n",
       "PAY_5                         0\n",
       "PAY_6                         0\n",
       "BILL_AMT1                     0\n",
       "BILL_AMT2                     0\n",
       "BILL_AMT3                     0\n",
       "BILL_AMT4                     0\n",
       "BILL_AMT5                     0\n",
       "BILL_AMT6                     0\n",
       "PAY_AMT1                      0\n",
       "PAY_AMT2                      0\n",
       "PAY_AMT3                      0\n",
       "PAY_AMT4                      0\n",
       "PAY_AMT5                      0\n",
       "PAY_AMT6                      0\n",
       "default payment next month    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()                                               #Check for missing data values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIMIT_BAL                        81\n",
       "SEX                               2\n",
       "EDUCATION                         7\n",
       "MARRIAGE                          4\n",
       "AGE                              56\n",
       "PAY_0                            11\n",
       "PAY_2                            11\n",
       "PAY_3                            11\n",
       "PAY_4                            11\n",
       "PAY_5                            10\n",
       "PAY_6                            10\n",
       "BILL_AMT1                     22723\n",
       "BILL_AMT2                     22346\n",
       "BILL_AMT3                     22026\n",
       "BILL_AMT4                     21548\n",
       "BILL_AMT5                     21010\n",
       "BILL_AMT6                     20604\n",
       "PAY_AMT1                       7943\n",
       "PAY_AMT2                       7899\n",
       "PAY_AMT3                       7518\n",
       "PAY_AMT4                       6937\n",
       "PAY_AMT5                       6897\n",
       "PAY_AMT6                       6939\n",
       "default payment next month        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()                                               #Returns the number of Distinctive Observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SEX'].unique()                                        #Checking the Unique Values in one Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIMIT_BAL                     int64\n",
       "SEX                           int64\n",
       "EDUCATION                     int64\n",
       "MARRIAGE                      int64\n",
       "AGE                           int64\n",
       "PAY_0                         int64\n",
       "PAY_2                         int64\n",
       "PAY_3                         int64\n",
       "PAY_4                         int64\n",
       "PAY_5                         int64\n",
       "PAY_6                         int64\n",
       "BILL_AMT1                     int64\n",
       "BILL_AMT2                     int64\n",
       "BILL_AMT3                     int64\n",
       "BILL_AMT4                     int64\n",
       "BILL_AMT5                     int64\n",
       "BILL_AMT6                     int64\n",
       "PAY_AMT1                      int64\n",
       "PAY_AMT2                      int64\n",
       "PAY_AMT3                      int64\n",
       "PAY_AMT4                      int64\n",
       "PAY_AMT5                      int64\n",
       "PAY_AMT6                      int64\n",
       "default payment next month    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes                                                 #Checking the Data Types of Values of each Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11888.0</td>\n",
       "      <td>163519.825034</td>\n",
       "      <td>136250.481355</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>240000.0</td>\n",
       "      <td>800000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18112.0</td>\n",
       "      <td>170086.462014</td>\n",
       "      <td>125231.777960</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>240000.0</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count           mean            std      min      25%       50%  \\\n",
       "SEX                                                                      \n",
       "1    11888.0  163519.825034  136250.481355  10000.0  50000.0  130000.0   \n",
       "2    18112.0  170086.462014  125231.777960  10000.0  70000.0  150000.0   \n",
       "\n",
       "          75%        max  \n",
       "SEX                       \n",
       "1    240000.0   800000.0  \n",
       "2    240000.0  1000000.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('SEX').LIMIT_BAL.describe()                    #Grouping Customers by each value (male and female here)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "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>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default payment next month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>30000.00000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>167484.322667</td>\n",
       "      <td>1.603733</td>\n",
       "      <td>1.853133</td>\n",
       "      <td>1.551867</td>\n",
       "      <td>35.485500</td>\n",
       "      <td>-0.016700</td>\n",
       "      <td>-0.133767</td>\n",
       "      <td>-0.166200</td>\n",
       "      <td>-0.220667</td>\n",
       "      <td>-0.266200</td>\n",
       "      <td>...</td>\n",
       "      <td>43262.948967</td>\n",
       "      <td>40311.400967</td>\n",
       "      <td>38871.760400</td>\n",
       "      <td>5663.580500</td>\n",
       "      <td>5.921163e+03</td>\n",
       "      <td>5225.68150</td>\n",
       "      <td>4826.076867</td>\n",
       "      <td>4799.387633</td>\n",
       "      <td>5215.502567</td>\n",
       "      <td>0.221200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>129747.661567</td>\n",
       "      <td>0.489129</td>\n",
       "      <td>0.790349</td>\n",
       "      <td>0.521970</td>\n",
       "      <td>9.217904</td>\n",
       "      <td>1.123802</td>\n",
       "      <td>1.197186</td>\n",
       "      <td>1.196868</td>\n",
       "      <td>1.169139</td>\n",
       "      <td>1.133187</td>\n",
       "      <td>...</td>\n",
       "      <td>64332.856134</td>\n",
       "      <td>60797.155770</td>\n",
       "      <td>59554.107537</td>\n",
       "      <td>16563.280354</td>\n",
       "      <td>2.304087e+04</td>\n",
       "      <td>17606.96147</td>\n",
       "      <td>15666.159744</td>\n",
       "      <td>15278.305679</td>\n",
       "      <td>17777.465775</td>\n",
       "      <td>0.415062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2326.750000</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.330000e+02</td>\n",
       "      <td>390.00000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>252.500000</td>\n",
       "      <td>117.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>140000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19052.000000</td>\n",
       "      <td>18104.500000</td>\n",
       "      <td>17071.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2.009000e+03</td>\n",
       "      <td>1800.00000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>240000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>54506.000000</td>\n",
       "      <td>50190.500000</td>\n",
       "      <td>49198.250000</td>\n",
       "      <td>5006.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>4505.00000</td>\n",
       "      <td>4013.250000</td>\n",
       "      <td>4031.500000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>891586.000000</td>\n",
       "      <td>927171.000000</td>\n",
       "      <td>961664.000000</td>\n",
       "      <td>873552.000000</td>\n",
       "      <td>1.684259e+06</td>\n",
       "      <td>896040.00000</td>\n",
       "      <td>621000.000000</td>\n",
       "      <td>426529.000000</td>\n",
       "      <td>528666.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            LIMIT_BAL           SEX     EDUCATION      MARRIAGE           AGE  \\\n",
       "count    30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean    167484.322667      1.603733      1.853133      1.551867     35.485500   \n",
       "std     129747.661567      0.489129      0.790349      0.521970      9.217904   \n",
       "min      10000.000000      1.000000      0.000000      0.000000     21.000000   \n",
       "25%      50000.000000      1.000000      1.000000      1.000000     28.000000   \n",
       "50%     140000.000000      2.000000      2.000000      2.000000     34.000000   \n",
       "75%     240000.000000      2.000000      2.000000      2.000000     41.000000   \n",
       "max    1000000.000000      2.000000      6.000000      3.000000     79.000000   \n",
       "\n",
       "              PAY_0         PAY_2         PAY_3         PAY_4         PAY_5  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean      -0.016700     -0.133767     -0.166200     -0.220667     -0.266200   \n",
       "std        1.123802      1.197186      1.196868      1.169139      1.133187   \n",
       "min       -2.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%       -1.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        8.000000      8.000000      8.000000      8.000000      8.000000   \n",
       "\n",
       "       ...      BILL_AMT4      BILL_AMT5      BILL_AMT6       PAY_AMT1  \\\n",
       "count  ...   30000.000000   30000.000000   30000.000000   30000.000000   \n",
       "mean   ...   43262.948967   40311.400967   38871.760400    5663.580500   \n",
       "std    ...   64332.856134   60797.155770   59554.107537   16563.280354   \n",
       "min    ... -170000.000000  -81334.000000 -339603.000000       0.000000   \n",
       "25%    ...    2326.750000    1763.000000    1256.000000    1000.000000   \n",
       "50%    ...   19052.000000   18104.500000   17071.000000    2100.000000   \n",
       "75%    ...   54506.000000   50190.500000   49198.250000    5006.000000   \n",
       "max    ...  891586.000000  927171.000000  961664.000000  873552.000000   \n",
       "\n",
       "           PAY_AMT2      PAY_AMT3       PAY_AMT4       PAY_AMT5  \\\n",
       "count  3.000000e+04   30000.00000   30000.000000   30000.000000   \n",
       "mean   5.921163e+03    5225.68150    4826.076867    4799.387633   \n",
       "std    2.304087e+04   17606.96147   15666.159744   15278.305679   \n",
       "min    0.000000e+00       0.00000       0.000000       0.000000   \n",
       "25%    8.330000e+02     390.00000     296.000000     252.500000   \n",
       "50%    2.009000e+03    1800.00000    1500.000000    1500.000000   \n",
       "75%    5.000000e+03    4505.00000    4013.250000    4031.500000   \n",
       "max    1.684259e+06  896040.00000  621000.000000  426529.000000   \n",
       "\n",
       "            PAY_AMT6  default payment next month  \n",
       "count   30000.000000                30000.000000  \n",
       "mean     5215.502567                    0.221200  \n",
       "std     17777.465775                    0.415062  \n",
       "min         0.000000                    0.000000  \n",
       "25%       117.750000                    0.000000  \n",
       "50%      1500.000000                    0.000000  \n",
       "75%      4000.000000                    0.000000  \n",
       "max    528666.000000                    1.000000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()                                            #Some statistical info of our data in each column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SEX', ylabel='Count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = df.drop(df.index[0])                              #Histogram of our data (drop the index column)\n",
    "sns.histplot(hist['SEX'])                                #Histogram of the sex count in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MARRIAGE', ylabel='Count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = df.drop(df.index[0])\n",
    "sns.histplot(hist['MARRIAGE'])                          #Histogram of the marriage count in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='default payment next month', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='default payment next month', data=df)  #Histogram of the 'default payment next month' count in this case\n",
    "#This is a much way better way than the previous method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='SEX'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SEX'].value_counts().plot(kind='pie', labels=['',''], \n",
    "                                        autopct='%1.1f%%', colors=['#B3CEDE','#4884AF'], \n",
    "                                        explode=[0,0.06], textprops={\"fontsize\":12})  #Pie Chart of 'sex' count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error value for Logistic Regression Model is: 0.22188888888888889\n",
      "Accuracy for Logistic Regression Model is: 0.7781111111111111\n"
     ]
    }
   ],
   "source": [
    "#'Features' contains the columns that this algorithm will take in and ignore the other columns in our data\n",
    "features = ['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE']\n",
    "\n",
    "\n",
    "x = df[features]                                        #X is the input values that we will put in our algorithm \n",
    "y = df['default payment next month']                    #Y is the output value that we have in our data\n",
    "\n",
    "#Training and Test Split is a step to split our data for training and testing\n",
    "#Our goal is to make our trained data as similar as possible to our test data\n",
    "#The more accurate it is, the better your algorithm is\n",
    "#Split can be any ratio but generally it 80:20 or 70:30\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.3, train_size= 0.7)\n",
    "\n",
    "lr = LogisticRegression()                               #Loading the Logistic Regression Function\n",
    "lr.fit(x_train, y_train)                                #Fitting our trained data with the ML Algorithm\n",
    "y_pred = lr.predict(x_test)                             #Predicting the test data with our trained data\n",
    "\n",
    "#There are different ways to find accuracy\n",
    "#RMS gives the value of how different the tested and trained datasets are\n",
    "#To find how similar they are, we calculate '1-rms' value\n",
    "#Root Mean Square method (RMS) is a better measure of accuaracy for Regression Algorithms\n",
    "rms1 = mean_squared_error(y_test , y_pred)              #Comparing the trained data and test data using RMS values\n",
    "mla1 = 1 - rms1                                         #Calculating the Accuracy (of how similar they are)\n",
    "\n",
    "#Printing our Accuracy\n",
    "print(\"Root Mean Square Error value for Logistic Regression Model is:\", rms1)\n",
    "print(\"Accuracy for Logistic Regression Model is:\", mla1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error value for Lasso Regression Model is: 0.16627878211868452\n",
      "Accuracy for Lasso Regression Model is: 0.8337212178813155\n"
     ]
    }
   ],
   "source": [
    "#'Features' contains the columns that this algorithm will take in and ignore the other columns in our data\n",
    "features = ['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE']\n",
    "\n",
    "\n",
    "x = df[features]                                        #X is the input values that we will put in our algorithm \n",
    "y = df['default payment next month']                    #Y is the output value that we have in our data\n",
    "\n",
    "#Training and Test Split is a step to split our data for training and testing\n",
    "#Our goal is to make our trained data as similar as possible to our test data\n",
    "#The more accurate it is, the better your algorithm is\n",
    "#Split can be any ratio but generally it 80:20 or 70:30\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.3, train_size= 0.7)\n",
    "\n",
    "ls = Lasso(alpha = 1)                                   #Loading the Logistic Regression Function\n",
    "ls.fit(x_train, y_train)                                #Fitting our trained data with the ML Algorithm\n",
    "y_pred = ls.predict(x_test)                             #Predicting the test data with our trained data\n",
    "\n",
    "#There are different ways to find accuracy\n",
    "#RMS gives the value of how different the tested and trained datasets are\n",
    "#To find how similar they are, we calculate '1-rms' value\n",
    "#Root Mean Square method (RMS) is a better measure of accuaracy for Regression Algorithms\n",
    "rms2 = mean_squared_error(y_test , y_pred)              #Comparing the trained data and test data using RMS values\n",
    "mla2 = 1 - rms2                                         #Calculating the Accuracy (of how similar they are)\n",
    "\n",
    "#Printing our Accuracy\n",
    "print(\"Root Mean Square Error value for Lasso Regression Model is:\", rms2)\n",
    "print(\"Accuracy for Lasso Regression Model is:\", mla2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error value for Ridge Regression Model is: 0.16771281215938966\n",
      "Accuracy for Ridge Regression Model is: 0.8322871878406104\n"
     ]
    }
   ],
   "source": [
    "#'Features' contains the columns that this algorithm will take in and ignore the other columns in our data\n",
    "features = ['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE']\n",
    "\n",
    "\n",
    "x = df[features]                                        #X is the input values that we will put in our algorithm \n",
    "y = df['default payment next month']                    #Y is the output value that we have in our data\n",
    "\n",
    "#Training and Test Split is a step to split our data for training and testing\n",
    "#Our goal is to make our trained data as similar as possible to our test data\n",
    "#The more accurate it is, the better your algorithm is\n",
    "#Split can be any ratio but generally it 80:20 or 70:30\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.3, train_size= 0.7)\n",
    "\n",
    "ri = Ridge(alpha = 1)                                   #Loading the Logistic Regression Function\n",
    "ri.fit(x_train, y_train)                                #Fitting our trained data with the ML Algorithm\n",
    "y_pred = ri.predict(x_test)                             #Predicting the test data with our trained data\n",
    "\n",
    "#There are different ways to find accuracy\n",
    "#RMS gives the value of how different the tested and trained datasets are\n",
    "#To find how similar they are, we calculate '1-rms' value\n",
    "#Root Mean Square method (RMS) is a better measure of accuaracy for Regression Algorithms\n",
    "rms3 = mean_squared_error(y_test , y_pred)              #Comparing the trained data and test data using RMS values\n",
    "mla3 = 1 - rms3                                         #Calculating the Accuracy (of how similar they are)\n",
    "\n",
    "#Printing our Accuracy\n",
    "print(\"Root Mean Square Error value for Ridge Regression Model is:\", rms3)\n",
    "print(\"Accuracy for Ridge Regression Model is:\", mla3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elastic Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error value for Elastic Net Model is: 0.16891604178364322\n",
      "Accuracy for Ridge Elastic Net Model is: 0.8310839582163567\n"
     ]
    }
   ],
   "source": [
    "#'Features' contains the columns that this algorithm will take in and ignore the other columns in our data\n",
    "features = ['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE']\n",
    "\n",
    "\n",
    "x = df[features]                                        #X is the input values that we will put in our algorithm \n",
    "y = df['default payment next month']                    #Y is the output value that we have in our data\n",
    "\n",
    "#Training and Test Split is a step to split our data for training and testing\n",
    "#Our goal is to make our trained data as similar as possible to our test data\n",
    "#The more accurate it is, the better your algorithm is\n",
    "#Split can be any ratio but generally it 80:20 or 70:30\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.3, train_size= 0.7)\n",
    "\n",
    "en = ElasticNet(alpha = 1)                              #Loading the Logistic Regression Function\n",
    "en.fit(x_train, y_train)                                #Fitting our trained data with the ML Algorithm\n",
    "y_pred = en.predict(x_test)                             #Predicting the test data with our trained data\n",
    "\n",
    "#There are different ways to find accuracy\n",
    "#RMS gives the value of how different the tested and trained datasets are\n",
    "#To find how similar they are, we calculate '1-rms' value\n",
    "#Root Mean Square method (RMS) is a better measure of accuaracy for Regression Algorithms\n",
    "rms4 = mean_squared_error(y_test , y_pred)              #Comparing the trained data and test data using RMS values\n",
    "mla4 = 1 - rms4                                         #Calculating the Accuracy (of how similar they are)\n",
    "\n",
    "#Printing our Accuracy\n",
    "print(\"Root Mean Square Error value for Elastic Net Model is:\", rms4)\n",
    "print(\"Accuracy for Ridge Elastic Net Model is:\", mla4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Random Forest Classifier Model is: 0.8185555555555556\n"
     ]
    }
   ],
   "source": [
    "#'Features' contains the columns that this algorithm will take in and ignore the other columns in our data\n",
    "features = ['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2',\n",
    "       'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
    "       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n",
    "       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6',]\n",
    "\n",
    "\n",
    "x = df[features]                                        #X is the input values that we will put in our algorithm \n",
    "y = df['default payment next month']                    #Y is the output value that we have in our data\n",
    "\n",
    "#Training and Test Split is a step to split our data for training and testing\n",
    "#Our goal is to make our trained data as similar as possible to our test data\n",
    "#The more accurate it is, the better your algorithm is\n",
    "#Split can be any ratio but generally it 80:20 or 70:30\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.3, train_size= 0.7)\n",
    "\n",
    "rfc=RandomForestClassifier(random_state=123)            #Loading the Random Forest Classifier Function\n",
    "rfc.fit(x_train, y_train)                               #Fitting our trained data with the ML Algorithm\n",
    "y_pred = rfc.predict(x_test)                            #Predicting the test data with our trained data\n",
    "\n",
    "#There are different ways to find accuracy\n",
    "#Accuracy Score gives the value of how similar the tested and trained datasets are\n",
    "#Accuracy Score is a better measure of accuracy for Classifier Algorithms\n",
    "mla5 = metrics.accuracy_score(y_test, y_pred)           #Calculating the Accuracy (of how similar they are)\n",
    "\n",
    "#Printing our Accuracy\n",
    "print(\"Accuracy for Random Forest Classifier Model is:\", mla5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Decision Tree Classifier Model is: 0.7241111111111111\n"
     ]
    }
   ],
   "source": [
    "#'Features' contains the columns that this algorithm will take in and ignore the other columns in our data\n",
    "features = ['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2',\n",
    "       'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
    "       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n",
    "       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6',]\n",
    "\n",
    "\n",
    "x = df[features]                                        #X is the input values that we will put in our algorithm \n",
    "y = df['default payment next month']                    #Y is the output value that we have in our data\n",
    "\n",
    "#Training and Test Split is a step to split our data for training and testing\n",
    "#Our goal is to make our trained data as similar as possible to our test data\n",
    "#The more accurate it is, the better your algorithm is\n",
    "#Split can be any ratio but generally it 80:20 or 70:30\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.3, train_size= 0.7)\n",
    "\n",
    "dtc = DecisionTreeClassifier(random_state=123)          #Loading the Decision Tree Classifier Function\n",
    "dtc.fit(x_train, y_train)                               #Fitting our trained data with the ML Algorithm\n",
    "y_pred = dtc.predict(x_test)                            #Predicting the test data with our trained data\n",
    "\n",
    "#There are different ways to find accuracy\n",
    "#Accuracy Score gives the value of how similar the tested and trained datasets are\n",
    "#Accuracy Score is a better measure of accuracy for Classifier Algorithms\n",
    "mla6 = metrics.accuracy_score(y_test, y_pred)           #Calculating the Accuracy (of how similar they are)\n",
    "\n",
    "#Printing our Accuracy\n",
    "print(\"Accuracy for Decision Tree Classifier Model is:\", mla6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the ML Algorithms with their accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = ['LR', 'L', 'R','EN', 'RFC', 'DTC']                 #X - Axis showing the ML Models\n",
    "accuracy = [mla1,mla2,mla3,mla4,mla5,mla6]                   #Y - Axis showing the corresponding accuracy scores\n",
    "sns.barplot(x = models, y = accuracy)                   #Bar plot comparing all the models\n",
    "\n",
    "#Note that the Accuracy is affected by various factors such as input values ('features') considered, training-testing split, randomness etc.\n",
    "#We need to work with different data and features to see what gives us the highest accuracy"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "1501c7299576f7dcec19c141229c1b202dd59653006f386b1f8bd9e61e128692"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}