Untitled1.ipynb

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        "<a href=\"https://colab.research.google.com/github/Emirhan-Denizyol/Customer-Churn-Records-Data-Analysis/blob/main/Untitled1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ]
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      "source": [
        "df = pd.read_csv('Customer-Churn-Records.csv')\n",
        "df"
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              "      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n",
              "0             1    15634602   Hargrave          619    France  Female   42   \n",
              "1             2    15647311       Hill          608     Spain  Female   41   \n",
              "2             3    15619304       Onio          502    France  Female   42   \n",
              "3             4    15701354       Boni          699    France  Female   39   \n",
              "4             5    15737888   Mitchell          850     Spain  Female   43   \n",
              "...         ...         ...        ...          ...       ...     ...  ...   \n",
              "9995       9996    15606229   Obijiaku          771    France    Male   39   \n",
              "9996       9997    15569892  Johnstone          516    France    Male   35   \n",
              "9997       9998    15584532        Liu          709    France  Female   36   \n",
              "9998       9999    15682355  Sabbatini          772   Germany    Male   42   \n",
              "9999      10000    15628319     Walker          792    France  Female   28   \n",
              "\n",
              "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
              "0          2       0.00              1          1               1   \n",
              "1          1   83807.86              1          0               1   \n",
              "2          8  159660.80              3          1               0   \n",
              "3          1       0.00              2          0               0   \n",
              "4          2  125510.82              1          1               1   \n",
              "...      ...        ...            ...        ...             ...   \n",
              "9995       5       0.00              2          1               0   \n",
              "9996      10   57369.61              1          1               1   \n",
              "9997       7       0.00              1          0               1   \n",
              "9998       3   75075.31              2          1               0   \n",
              "9999       4  130142.79              1          1               0   \n",
              "\n",
              "      EstimatedSalary  Exited  Complain  Satisfaction Score Card Type  \\\n",
              "0           101348.88       1         1                   2   DIAMOND   \n",
              "1           112542.58       0         1                   3   DIAMOND   \n",
              "2           113931.57       1         1                   3   DIAMOND   \n",
              "3            93826.63       0         0                   5      GOLD   \n",
              "4            79084.10       0         0                   5      GOLD   \n",
              "...               ...     ...       ...                 ...       ...   \n",
              "9995         96270.64       0         0                   1   DIAMOND   \n",
              "9996        101699.77       0         0                   5  PLATINUM   \n",
              "9997         42085.58       1         1                   3    SILVER   \n",
              "9998         92888.52       1         1                   2      GOLD   \n",
              "9999         38190.78       0         0                   3   DIAMOND   \n",
              "\n",
              "      Point Earned  \n",
              "0              464  \n",
              "1              456  \n",
              "2              377  \n",
              "3              350  \n",
              "4              425  \n",
              "...            ...  \n",
              "9995           300  \n",
              "9996           771  \n",
              "9997           564  \n",
              "9998           339  \n",
              "9999           911  \n",
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              "[10000 rows x 18 columns]"
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              "      <td>339</td>\n",
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              "      <td>3</td>\n",
              "      <td>DIAMOND</td>\n",
              "      <td>911</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>10000 rows × 18 columns</p>\n",
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      "source": [
        "df.drop(\"RowNumber\", axis=1, inplace=True)\n",
        "df"
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "      CustomerId    Surname  CreditScore Geography  Gender  Age  Tenure  \\\n",
              "0       15634602   Hargrave          619    France  Female   42       2   \n",
              "1       15647311       Hill          608     Spain  Female   41       1   \n",
              "2       15619304       Onio          502    France  Female   42       8   \n",
              "3       15701354       Boni          699    France  Female   39       1   \n",
              "4       15737888   Mitchell          850     Spain  Female   43       2   \n",
              "...          ...        ...          ...       ...     ...  ...     ...   \n",
              "9995    15606229   Obijiaku          771    France    Male   39       5   \n",
              "9996    15569892  Johnstone          516    France    Male   35      10   \n",
              "9997    15584532        Liu          709    France  Female   36       7   \n",
              "9998    15682355  Sabbatini          772   Germany    Male   42       3   \n",
              "9999    15628319     Walker          792    France  Female   28       4   \n",
              "\n",
              "        Balance  NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
              "0          0.00              1          1               1        101348.88   \n",
              "1      83807.86              1          0               1        112542.58   \n",
              "2     159660.80              3          1               0        113931.57   \n",
              "3          0.00              2          0               0         93826.63   \n",
              "4     125510.82              1          1               1         79084.10   \n",
              "...         ...            ...        ...             ...              ...   \n",
              "9995       0.00              2          1               0         96270.64   \n",
              "9996   57369.61              1          1               1        101699.77   \n",
              "9997       0.00              1          0               1         42085.58   \n",
              "9998   75075.31              2          1               0         92888.52   \n",
              "9999  130142.79              1          1               0         38190.78   \n",
              "\n",
              "      Exited  Complain  Satisfaction Score Card Type  Point Earned  \n",
              "0          1         1                   2   DIAMOND           464  \n",
              "1          0         1                   3   DIAMOND           456  \n",
              "2          1         1                   3   DIAMOND           377  \n",
              "3          0         0                   5      GOLD           350  \n",
              "4          0         0                   5      GOLD           425  \n",
              "...      ...       ...                 ...       ...           ...  \n",
              "9995       0         0                   1   DIAMOND           300  \n",
              "9996       0         0                   5  PLATINUM           771  \n",
              "9997       1         1                   3    SILVER           564  \n",
              "9998       1         1                   2      GOLD           339  \n",
              "9999       0         0                   3   DIAMOND           911  \n",
              "\n",
              "[10000 rows x 17 columns]"
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              "      <th>9999</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 17 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-af2458c7-889e-4a04-a800-4e77d1752f56')\"\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "5lU6ACND1zWj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9baf912-0bec-4f20-e0f8-db7b2b0da57a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerId            0\n",
              "Surname               0\n",
              "CreditScore           0\n",
              "Geography             0\n",
              "Gender                0\n",
              "Age                   0\n",
              "Tenure                0\n",
              "Balance               0\n",
              "NumOfProducts         0\n",
              "HasCrCard             0\n",
              "IsActiveMember        0\n",
              "EstimatedSalary       0\n",
              "Exited                0\n",
              "Complain              0\n",
              "Satisfaction Score    0\n",
              "Card Type             0\n",
              "Point Earned          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Here I chose the top 10 customers with the highest balances in the bank. Since the number of customers is high, I increased the readability by buying 10.**"
      ],
      "metadata": {
        "id": "J_1AwG5BGemG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(18, 8))\n",
        "plt.bar(df[['Balance', 'Surname']].sort_values(by=['Balance'], ascending=False).head(10).iloc[range(10), 1], df[['Balance', 'Surname']].sort_values(by=['Balance'], ascending=False).head(10).iloc[range(10), 0])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "K06zCXvuF3aW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "outputId": "2967727e-e4f7-49e9-cde1-dc7b71c8b7c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x800 with 1 Axes>"
            ],
            "image/png": 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AAKAYojgAAAAAAMUQxQEAAAAAKIYoDgAAAABAMURxAAAAAACKIYoDAAAAAFAMURwAAAAAgGKI4gAAAAAAFEMUBwAAAACgGKI4AAAAAADFEMUBAAAAACiGKA4AAAAAQDFEcQAAAAAAiiGKAwAAAABQDFEcAAAAAIBiiOIAAAAAABRDFAcAAAAAoBiiOAAAAAAAxRDFAQAAAAAohigOAAAAAEAxRHEAAAAAAIohigMAAAAAUAxRHAAAAACAYojiAAAAAAAUQxQHAAAAAKAYojgAAAAAAMUQxQEAAAAAKIYoDgAAAABAMURxAAAAAACKIYoDAAAAAFAMURwAAAAAgGKI4gAAAAAAFEMUBwAAAACgGKI4AAAAAADFEMUBAAAAACiGKA4AAAAAQDFEcQAAAAAAiiGKAwAAAABQDFEcAAAAAIBiiOIAAAAAABRDFAcAAAAAoBiiOAAAAAAAxRDFAQAAAAAohigOAAAAAEAxRHEAAAAAAIohigMAAAAAUAxRHAAAAACAYojiAAAAAAAUQxQHAAAAAKAYojgAAAAAAMUQxQEAAAAAKIYoDgAAAABAMURxAAAAAACKIYoDAAAAAFAMURwAAAAAgGKI4gAAAAAAFEMUBwAAAACgGKI4AAAAAADFEMUBAAAAACiGKA4AAAAAQDFEcQAAAAAAiiGKAwAAAABQDFEcAAAAAIBiiOIAAAAAABRDFAcAAAAAoBhFRPHx48enW7duad68efr06ZP7779/fS8JAAAAAID14GMfxa+99tqMGjUqp59+eh566KH06tUr9fX1mTdv3vpeGgAAAAAAH7KN1vcC1rULLrggRx11VIYOHZokufzyyzNx4sRcccUVOfnkkxvNLl68OIsXL65eX7BgQZKkoaHhw1vwBmbZ4n+s7yXwHj6s49IxsGFzHOAYIPlwjgPHwIbNcwGOARLHAY4B3ua1IaW2zOWPu1KprHK2prI6Ux9RS5YsScuWLfOrX/0qgwYNqt4+ZMiQzJ8/PzfddFOj+XHjxuV73/veh7xKAAAAAADWhueffz6bb775Smc+1u8Uf/nll7N06dJ07Nix0e0dO3bMk08++Y75U045JaNGjapeX7ZsWV599dVsuummqampWefrZd1qaGhI165d8/zzz6eurm59L4f1wDGAY4DEcYBjgLc5DnAM4BggcRzgGPg4qVQq+fvf/54uXbqscvZjHcXXVG1tbWpraxvd1q5du/WzGNaZuro6T3KFcwzgGCBxHOAY4G2OAxwDOAZIHAc4Bj4u2rZtu1pzH+tftLnZZpuladOmmTt3bqPb586dm06dOq2nVQEAAAAAsL58rKN4s2bN0rt370yZMqV627JlyzJlypT07dt3Pa4MAAAAAID14WN/+pRRo0ZlyJAh2WWXXbLrrrvmoosuyqJFizJ06ND1vTQ+ZLW1tTn99NPfcYocyuEYwDFA4jjAMcDbHAc4BnAMkDgOcAyUqqZSqVTW9yLWtUsvvTTnnXde5syZk5122imXXHJJ+vTps76XBQAAAADAh6yIKA4AAAAAAMnH/JziAAAAAACwIlEcAAAAAIBiiOIAAAAAABRDFAegCP369cvIkSNXOtOtW7dcdNFFH8p6+HiaOnVqampqMn/+/PW9lKLV1NTkxhtvXN/LYAOy4jHx7LPPpqamJjNnzkziz21JVvXc4FiADZs/o8DaJIrzsXPEEUdk0KBB63sZrAU1NTUrvYwbN26N9nfEEUdUP3bjjTdO9+7dc9JJJ+WNN95Y7X14IbZuvdefX5/3j6/lfy6POeaYd2wbPnx4ampqcsQRR6zWvtY0hE6YMCHt2rVb7Xk2HC+99FKOPfbYbLHFFqmtrU2nTp1SX1+fu+++e30vjQ/ZP//d3rFjx3zxi1/MFVdckWXLllXnXnzxxey3337rcaV8GObMmZPjjjsun/rUp1JbW5uuXbvmgAMOyJQpU1br43fbbbe8+OKLadu27TpeKR/E2nztwIZjbX/vR9k8T7A6RHFgg/Xiiy9WLxdddFHq6uoa3fad73xnjfc5YMCAvPjii/nrX/+aCy+8MP/1X/+V008/fR2sHlhdXbt2zS9/+cu8/vrr1dveeOONXHPNNdliiy3W48rYUA0ePDgPP/xwrrrqqvz5z3/OzTffnH79+uWVV15Z30tjPVj+d/uzzz6b3/3ud9lrr71y/PHH50tf+lLeeuutJEmnTp1SW1u7nlfKuvTss8+md+/eueOOO3Leeefl0UcfzaRJk7LXXntl+PDhq7WPZs2apVOnTqmpqVnHq+WDWp+vHZYsWbJO91+qdfG9H2XzPQarIopTlGnTpmXXXXdNbW1tOnfunJNPPrn6zRIbnk6dOlUvbdu2TU1NTfV6hw4dcsEFF2TzzTdPbW1tdtppp0yaNGmV+1z+jsKuXbtm0KBB6d+/fyZPnlzdvnjx4nz7299Ohw4d0rx58+yxxx554IEHkrz9zdZee+2VJGnfvr1/XV5PXnnllRxyyCH55Cc/mZYtW6Znz575xS9+0Whm0aJFOfzww9O6det07tw5559//jv2M2/evBxwwAFp0aJFunfvnquvvvodM88991wOPPDAtG7dOnV1dfna176WuXPnVrePGzcuO+20U/77v/873bp1S9u2bXPwwQfn73//+9p/4B9jO++8c7p27ZobbrihetsNN9yQLbbYIp/97Gerty1btiznnntuttpqq9TW1maLLbbI97///Xfd5/LTI9xwww3Za6+90rJly/Tq1SvTp09P8vZPHwwdOjQLFix4xzuQXnvttRx++OFp3759WrZsmf322y9PPfVUdd//+7//mwMOOCDt27dPq1atsv322+fWW29tdP8zZszILrvskpYtW2a33XbL7Nmz19anq3jz58/PH/7wh/zgBz/IXnvtlS233DK77rprTjnllPzrv/5rde7ll1/Ol7/85bRs2TJbb711br755uq2pUuXZtiwYenevXtatGiRbbbZJhdffHF1+2OPPZYmTZrkpZdeSpK8+uqradKkSQ4++ODqzFlnnZU99tjjQ3jErMryv9s/+clPZuedd86pp56am266Kb/73e8yYcKEJGv2kyT/+Mc/st9++2X33Xev/oTST3/60/To0SPNmzfPtttumx/96Efr5sHwvn3rW99KTU1N7r///gwePDif+cxnsv3222fUqFG59957q3Mre274559MW53XHKwfq/vaYWWv7Zdb1feI/fr1y4gRIzJy5Mhsttlmqa+vT/L23xX77bdfWrdunY4dO+awww7Lyy+/vI4f+cfXyr7369SpU1q3bl2dXdXrrMsuuyyf/vSn06xZs2yzzTb57//+70bba2pq8l//9V/50pe+lJYtW6ZHjx6ZPn16/vKXv6Rfv35p1apVdttttzz99NPVj/nTn/6UvfbaK23atEldXV169+6dBx98sLr917/+dbbffvvU1tamW7du7/j+o1u3bvmP//iPfOMb30ibNm2yxRZb5Mc//vHa/BTyT1b3eWLSpEnZY4890q5du2y66ab50pe+1OhrnySPPvpo9t5777Ro0SKbbrppjj766CxcuPBDeyysG6I4xfi///u/7L///vnc5z6XP/3pT7nsssvys5/9LGedddb6Xhrvw8UXX5zzzz8/P/zhD/PII4+kvr4+//qv/9ooXK3KY489lnvuuSfNmjWr3nbSSSfl17/+da666qo89NBD2WqrrVJfX59XX301Xbt2za9//eskyezZs/Piiy82iih8ON5444307t07EydOzGOPPZajjz46hx12WO6///7qzOjRozNt2rTcdNNNuf322zN16tQ89NBDjfZzxBFH5Pnnn8+dd96ZX/3qV/nRj36UefPmVbcvW7YsBx54YF599dVMmzYtkydPzl//+tccdNBBjfbz9NNP58Ybb8wtt9ySW265JdOmTcs555yzbj8JH0Pf+MY3cuWVV1avX3HFFRk6dGijmVNOOSXnnHNOTjvttDzxxBO55ppr0rFjx5Xu9//9v/+X73znO5k5c2Y+85nP5JBDDslbb72V3Xbb7R3vQlr+DqQjjjgiDz74YG6++eZMnz49lUol+++/f958880kb//I5eLFi3PXXXfl0UcfzQ9+8ING36gtv9/zzz8/Dz74YDbaaKN84xvfWBufJpK0bt06rVu3zo033pjFixe/59z3vve9fO1rX8sjjzyS/fffP4ceemheffXVJG//+d58881z/fXX54knnsjYsWNz6qmn5rrrrkuSbL/99tl0000zbdq0JMkf/vCHRteTtyNKv3791t0D5QPZe++906tXr0bfCK+O+fPn54tf/GKWLVuWyZMnp127drn66qszduzYfP/738+sWbPyH//xHznttNNy1VVXraPVs6ZeffXVTJo0KcOHD0+rVq3esX3FU2Wt7Lnhn63Oaw7Wn9V57bCy1/bJ6n+PeNVVV6VZs2a5++67c/nll2f+/PnZe++989nPfjYPPvhgJk2alLlz5+ZrX/vaun/grPR11m9+85scf/zxOfHEE/PYY4/lm9/8ZoYOHZo777yz0T7OPPPMHH744Zk5c2a23XbbfP3rX883v/nNnHLKKXnwwQdTqVQyYsSI6vyhhx6azTffPA888EBmzJiRk08+ORtvvHGStyP91772tRx88MF59NFHM27cuJx22mnVf5hd7vzzz88uu+yShx9+ON/61rdy7LHHeuPEOrY6zxOLFi3KqFGj8uCDD2bKlClp0qRJvvzlL1dPw7Zo0aLU19enffv2eeCBB3L99dfn97//faPjg4+oCnzMDBkypHLggQe+4/ZTTz21ss0221SWLVtWvW38+PGV1q1bV5YuXfohrpD348orr6y0bdu2er1Lly6V73//+41mPve5z1W+9a1vvec+hgwZUmnatGmlVatWldra2kqSSpMmTSq/+tWvKpVKpbJw4cLKxhtvXLn66qurH7NkyZJKly5dKueee26lUqlU7rzzzkqSymuvvbb2HhxVK36NVrw0b958pZ/3gQMHVk488cRKpVKp/P3vf680a9asct1111W3v/LKK5UWLVpUjj/++EqlUqnMnj27kqRy//33V2dmzZpVSVK58MILK5VKpXL77bdXmjZtWnnuueeqM48//nijjzv99NMrLVu2rDQ0NFRnRo8eXenTp8/a+HQUYflz9rx58yq1tbWVZ599tvLss89WmjdvXnnppZcqBx54YGXIkCGVhoaGSm1tbeUnP/nJe+4rSeU3v/lNpVKpVJ555plKkspPf/rT6vblX79Zs2ZVKpV3Pq9UKpXKn//850qSyt1331297eWXX660aNGiekz17NmzMm7cuHddw/LniN///vfV2yZOnFhJUnn99dfX6HPDe/vVr35Vad++faV58+aV3XbbrXLKKadU/vSnP1W3J6mMGTOmen3hwoWVJJXf/e5377nP4cOHVwYPHly9/pWvfKUyfPjwSqVSqYwcObIyevToSvv27SuzZs2qLFmypNKyZcvK7bffvg4eHWvivV73VSqVykEHHVTp0aNHpVJ59+eHhx9+uFKp/P9/bmfNmlXZcccdK4MHD64sXry4up9Pf/rTlWuuuabRvs8888xK37591/rj4f257777KkkqN9xww0rnVvXcsDqv81Z8zcH6sbqvHVbntf3qfI/4hS98ofLZz3620RrOPPPMyr777tvotueff76SpDJ79ux19dCL8W6v0SqV1Xudtdtuu1WOOuqoRh/31a9+tbL//vtXr//zc8H06dMrSSo/+9nPqrf94he/qDRv3rx6vU2bNpUJEya863q//vWvV774xS82um306NGV7bbbrnp9yy23rPz7v/979fqyZcsqHTp0qFx22WXvuk8+mNV9nng3L730UiVJ5dFHH61UKpXKj3/840r79u0rCxcurM5MnDix0qRJk8qcOXM+jIfDOuKd4hRj1qxZ6du3b6NzBO6+++5ZuHBh/va3v63HlbGmGhoa8sILL2T33XdvdPvuu++eWbNmrfRj99prr8ycOTP33XdfhgwZkqFDh2bw4MFJ3n7H75tvvtlovxtvvHF23XXXVe6XtWf512jFy09/+tPq9qVLl+bMM89Mz549s8kmm6R169a57bbb8txzzyV5++u4ZMmS9OnTp/oxm2yySbbZZpvq9VmzZmWjjTZK7969q7dtu+22jd5JNmvWrHTt2jVdu3at3rbddtulXbt2jY6Hbt26pU2bNtXrnTt3bvSOc1bPJz7xiQwcODATJkzIlVdemYEDB2azzTarbp81a1YWL16cffbZZ432u+OOO1b/v3Pnzkmy0q/P8mNjxeNn0003zTbbbFP9un/729/OWWedld133z2nn356HnnkkQ98v6yZwYMH54UXXsjNN9+cAQMGZOrUqdl5550bvSNrxa9Bq1atUldX1+hrMH78+PTu3Tuf+MQn0rp16/z4xz+uPo8kyRe+8IVMnTo1ydvvCt97772z5557ZurUqXnggQfe8fcFG55KpbJG54b+4he/mK222irXXntt9afIFi1alKeffjrDhg2r/pRC69atc9ZZZ73jR6tZfyqVymrPruq5YUWres3B+rWq1w6r89p+db9HXPE1Y/L2qTTuvPPORs8L2267bfV+WbdW9jpr1qxZq/V94or7WP6Thz179mx02xtvvJGGhoYkyahRo3LkkUemf//+Oeeccxp9nd/rPp966qksXbr0Xe9z+elhvD5ct1b1PJEkTz31VA455JB86lOfSl1dXbp165Yk1ef6WbNmpVevXo1+Emn33XfPsmXLvNP/I26j9b0AgA9Tq1atstVWWyV5+0enevXqlZ/97GcZNmzYel4Zy634NVpuxW9KzjvvvFx88cW56KKL0rNnz7Rq1SojR45cb7/0aPmPTS5XU1NT/VE71sw3vvGN6o8hjh8/vtG2Fi1avK99rvj1Wf4N7wf9+hx55JGpr6/PxIkTc/vtt+fss8/O+eefn+OOO26d3i+NNW/ePF/84hfzxS9+MaeddlqOPPLInH766dXf9bCyP5u//OUv853vfCfnn39++vbtmzZt2uS8887LfffdV53v169fRo4cmaeeeipPPPFE9thjjzz55JOZOnVqXnvtteq5TNlwzZo1K927d1/t+YEDB+bXv/51nnjiiWoYWX6+0J/85CeN/rEsSZo2bbr2FssHsvXWW6empiZPPvnkKmfX5O/tDe01B++0stcOa9M/n5Zn4cKFOeCAA/KDH/zgHbPLIy3rztp4nfVu+1jZfseNG5evf/3rmThxYn73u9/l9NNPzy9/+ct8+ctffl/3ufw+vD5c91b1PHHAAQdkyy23zE9+8pN06dIly5Ytyw477OC5vgDeKU4xlv/yjBXfSXL33XenTZs22XzzzdfjylhTdXV16dKlS+6+++5Gt999993ZbrvtVns/TZo0yamnnpoxY8bk9ddfr/4ylhX3++abb+aBBx6o7nf5O8dW/Bd/Plx33313DjzwwPz7v/97evXqlU996lP585//XN3+6U9/OhtvvHGjuPXaa681mtl2223z1ltvZcaMGdXbZs+eXf3FWsnbzxnPP/98nn/++eptTzzxRObPn79Gxxmrb8CAAVmyZEnefPPN6i+xWm7rrbdOixYtMmXKlLV2f82aNXvHn+UePXrkrbfeanT8vPLKK5k9e3ajr3vXrl1zzDHH5IYbbsiJJ56Yn/zkJ2ttXbw/2223XRYtWrRas3fffXd22223fOtb38pnP/vZbLXVVu94Z1/Pnj3Tvn37nHXWWdlpp53SunXr9OvXL9OmTcvUqVOdT3wDd8cdd+TRRx+t/jTY6jjnnHMyZMiQ7LPPPnniiSeSvP1OwS5duuSvf/1rttpqq0aXNQnurFubbLJJ6uvrM378+Hd9Hljx7/c1sarXHKx/K3vtsDqv7d/v94g777xzHn/88XTr1u0dzw3vdl57Pjw9evT4wN8nvpfPfOYzOeGEE3L77bfnK1/5SvVc1e91n5/5zGf8A+oGYGXPE8tf548ZMyb77LNPevTokddee63RTI8ePfKnP/2p0d8vd999d5o0adLop5H56BHF+VhasGDBO06/cPTRR+f555/PcccdlyeffDI33XRTTj/99IwaNSpNmvij8FEzevTo/OAHP8i1116b2bNn5+STT87MmTNz/PHHr9F+vvrVr6Zp06YZP358WrVqlWOPPTajR4/OpEmT8sQTT+Soo47KP/7xj+o7ybfccsvU1NTklltuyUsvveQ3Tq8HW2+9dSZPnpx77rkns2bNyje/+c3MnTu3ur1169YZNmxYRo8enTvuuCOPPfZYjjjiiEZ/zrfZZpsMGDAg3/zmN3PfffdlxowZOfLIIxu9G7l///7p2bNnDj300Dz00EO5//77c/jhh+cLX/hCdtlllw/1MZeiadOmmTVrVp544ol3fAPRvHnzfPe7381JJ52Un//853n66adz77335mc/+9n7vr9u3bpl4cKFmTJlSl5++eX84x//yNZbb50DDzwwRx11VP74xz/mT3/6U/793/89n/zkJ3PggQcmSUaOHJnbbrstzzzzTB566KHceeed6dGjxwd67Ky+V155JXvvvXf+53/+J4888kieeeaZXH/99Tn33HOrX6NV2XrrrfPggw/mtttuy5///OecdtppeeCBBxrN1NTUZM8998zVV19dDeA77rhjFi9enClTpuQLX/jC2n5ovE+LFy/OnDlz8n//93956KGH8h//8R858MAD86UvfSmHH374Gu3rhz/8YQ499NDsvffe1Xcdf+9738vZZ5+dSy65JH/+85/z6KOP5sorr8wFF1ywLh4O79P48eOzdOnS7Lrrrvn1r3+dp556KrNmzcoll1ySvn37vq99ruo1B+vfyl47rM5r+29961vv63vE4cOH59VXX80hhxySBx54IE8//XRuu+22DB061Jtn1rPRo0dnwoQJueyyy/LUU0/lggsuyA033FD9hervx+uvv54RI0Zk6tSp+d///d/cfffdeeCBB6qv/0488cRMmTIlZ555Zv785z/nqquuyqWXXvqB7pO1Z2XPE+3bt8+mm26aH//4x/nLX/6SO+64I6NGjWo0c+ihh6Z58+YZMmRIHnvssdx555057rjjcthhh1VPvcNHk9On8LE0derUfPazn21027Bhw3Lrrbdm9OjR6dWrVzbZZJMMGzYsY8aMWU+r5IP49re/nQULFuTEE0/MvHnzst122+Xmm2/O1ltvvUb72WijjTJixIice+65OfbYY3POOedk2bJlOeyww/L3v/89u+yyS2677ba0b98+SfLJT34y3/ve93LyySdn6NChOfzww9/xW8VZt8aMGZO//vWvqa+vT8uWLXP00Udn0KBBWbBgQXXmvPPOq/5Ya5s2bXLiiSc22p4kV155ZY488sh84QtfSMeOHXPWWWfltNNOq26vqanJTTfdlOOOOy577rlnmjRpkgEDBuQ///M/P7THWqK6urr33Hbaaadlo402ytixY/PCCy+kc+fOOeaYY973fe2222455phjctBBB+WVV17J6aefnnHjxuXKK6/M8ccfny996UtZsmRJ9txzz9x6663VH3ldunRphg8fnr/97W+pq6vLgAEDcuGFF77vdbBmWrdunT59+uTCCy+sni+2a9euOeqoo3Lqqaeu1j6++c1v5uGHH85BBx2UmpqaHHLIIfnWt76V3/3ud43mvvCFL+TGG2+sRvEmTZpkzz33zMSJE51PfAMyadKkdO7cORtttFHat2+fXr165ZJLLsmQIUPe1xsfLrzwwixdujR77713pk6dmiOPPDItW7bMeeedl9GjR6dVq1bp2bNnRo4cufYfDO/bpz71qTz00EP5/ve/nxNPPDEvvvhiPvGJT6R379657LLL3tc+V+c1B+vfyl47rM5r+/fzPeLyn1r97ne/m3333TeLFy/OlltumQEDBnjD1Xo2aNCgXHzxxfnhD3+Y448/Pt27d8+VV175gX7Cq2nTpnnllVdy+OGHZ+7cudlss83yla98Jd/73veSvP2TA9ddd13Gjh2bM888M507d84ZZ5xRPaUb6997PU80adIkv/zlL/Ptb387O+ywQ7bZZptccskljY6Xli1b5rbbbsvxxx+fz33uc2nZsmUGDx7sH8c/Bmoqa/JbSQAAAAAA4CPMP2ECAAAAAFAMURwAAAAAgGKI4gAAAAAAFEMUBwAAAACgGKI4AAAAAADFEMUBAAAAACiGKA4AAAAAQDFEcQAAAAAAiiGKAwAAAABQDFEcAAAAAIBiiOIAAAAAABTj/wOW9sYLWcqMawAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Here I made a comparison by averaging their credit scores and balances by country**\n",
        "**Here, I preferred this way instead of the index method to select the countries. I avoided duplication using the 'drop_duplicates()' method as it kept repeating when I imported countries. So I chose it with the 'iloc[]' method.**"
      ],
      "metadata": {
        "id": "l7TJ2l1QIDJ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "credit_score = df.groupby('Geography')['CreditScore'].mean()\n",
        "credit_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQE_RoqRKV6C",
        "outputId": "3a840d5c-3ca1-45a2-e0ae-f6ab0176fd14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Geography\n",
              "France     649.668329\n",
              "Germany    651.453567\n",
              "Spain      651.333872\n",
              "Name: CreditScore, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "balance = df.groupby('Geography')['Balance'].mean()\n",
        "balance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9BIcemTKggh",
        "outputId": "a1c76240-80a5-4a64-d8f6-ed830fca4588"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Geography\n",
              "France      62092.636516\n",
              "Germany    119730.116134\n",
              "Spain       61818.147763\n",
              "Name: Balance, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "credit_score = df.groupby('Geography')['CreditScore'].mean()\n",
        "balance = df.groupby('Geography')['Balance'].mean()\n",
        "country = []\n",
        "\n",
        "for i in range(3):                                                  # \n",
        "    country.append(df['Geography'].drop_duplicates().iloc[i])\n",
        "\n",
        "fig = plt.figure(figsize=(16, 8))\n",
        "\n",
        "first_graphic = fig.add_subplot(1, 2, 1)\n",
        "first_graphic.bar(country, balance)\n",
        "first_graphic.set_title('Balances by Country')\n",
        "plt.xlabel('Country')\n",
        "plt.ylabel('Balance')\n",
        "\n",
        "second_graphic = fig.add_subplot(1, 2, 2)\n",
        "second_graphic.plot(country, credit_score)\n",
        "second_graphic.set_title('Credit Scores By Country')\n",
        "plt.xlabel('Country')\n",
        "plt.ylabel('Credit Score')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "1uD_H8QFGYJU",
        "outputId": "a045abcf-e64e-45ee-f273-99084b8924ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**I showed the gender distribution of the customers using the bank with a graph.**"
      ],
      "metadata": {
        "id": "nhNvQngYKDUf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Gender'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8mKabYW2Hk3d",
        "outputId": "577bd933-605d-4904-9d57-6f032cc84080"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male      5457\n",
              "Female    4543\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Gender'] == 'Female'].groupby('Gender')['Geography'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SnA_6kmVKG8s",
        "outputId": "52fa2d6f-1219-4c98-8895-4edc9ba67cba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender  Geography\n",
              "Female  France       2261\n",
              "        Germany      1193\n",
              "        Spain        1089\n",
              "Name: Geography, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Gender'] == 'Female'].groupby('Gender')['Geography'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYz7B3NhKRDS",
        "outputId": "14f6d30e-26b2-4e17-d80e-263e1faed621"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender  Geography\n",
              "Female  France       2261\n",
              "        Germany      1193\n",
              "        Spain        1089\n",
              "Name: Geography, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numbers_of_female = []\n",
        "for i in range(3):\n",
        "    numbers_of_female.append(df[df['Gender'] == 'Female'].groupby('Gender')['Geography'].value_counts().iloc[i])\n",
        "\n",
        "numbers_of_male = []\n",
        "for i in range(3):\n",
        "    numbers_of_male.append(df[df['Gender'] == 'Female'].groupby('Gender')['Geography'].value_counts().iloc[i])\n",
        "\n",
        "fig = plt.figure(figsize=(16, 8))\n",
        "fg = fig.add_subplot(1, 3, 1)\n",
        "fg.bar([df['Gender'].drop_duplicates().iloc[0], df['Gender'].drop_duplicates().iloc[1]], [df['Gender'].value_counts().iloc[1], df['Gender'].value_counts().iloc[0]])\n",
        "plt.xlabel('Gender')\n",
        "plt.ylabel('Values')\n",
        "\n",
        "sg = fig.add_subplot(1, 3, 2)\n",
        "sg.bar(country, numbers_of_female)\n",
        "plt.xlabel('Country')\n",
        "plt.ylabel('Values')\n",
        "plt.title('Number Of Female')\n",
        "\n",
        "td = fig.add_subplot(1, 3, 3)\n",
        "td.bar(country, numbers_of_male)\n",
        "plt.xlabel('Country')\n",
        "plt.ylabel('Values')\n",
        "plt.title('Number Of Male')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "j_G5xfwyJedF",
        "outputId": "36736d03-4aa0-4f7e-a49f-d9fdd75ed01a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Average ages of customers using the bank**"
      ],
      "metadata": {
        "id": "Fc2RU_MOLYLi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ages = df['Age'].value_counts().sort_values(ascending=False)\n",
        "ages"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eCsm77BTLAFJ",
        "outputId": "a6a5cc9d-4940-4cf9-a3e5-4e0aaafed8b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37    478\n",
              "38    477\n",
              "35    474\n",
              "36    456\n",
              "34    447\n",
              "     ... \n",
              "92      2\n",
              "82      1\n",
              "88      1\n",
              "85      1\n",
              "83      1\n",
              "Name: Age, Length: 70, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Above, I chose another way instead of the 'index()' method. At this point, I used the index method that made that path. And I made my process shorter and simpler.**"
      ],
      "metadata": {
        "id": "Xznwqc18Lhfr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(15, 5))\n",
        "plt.bar(ages.index, ages)\n",
        "plt.title('Age Distribution')\n",
        "plt.ylabel('The Number Of Customer')\n",
        "plt.xlabel('Ages')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "bp8BDPIxLCzS",
        "outputId": "28d2d573-5177-4560-9ea3-fee0b088ecf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Customers tenure of average the bank card.**"
      ],
      "metadata": {
        "id": "N2zYmoNhMhuy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tenure = df['Tenure'].sort_values().value_counts()\n",
        "tenure"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VQ6Vsa8LGhy",
        "outputId": "5e464535-e00c-4348-a2d5-bcd0a094121a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2     1048\n",
              "1     1035\n",
              "7     1028\n",
              "8     1025\n",
              "5     1012\n",
              "3     1009\n",
              "4      989\n",
              "9      984\n",
              "6      967\n",
              "10     490\n",
              "0      413\n",
              "Name: Tenure, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(tenure.index, tenure)\n",
        "plt.xlabel('Usage Period')\n",
        "plt.ylabel('Number Of Users')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "qsxsLUkcMT9v",
        "outputId": "c821e81b-8943-4237-a6b0-112a28911b93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Averages of preferred card types.**"
      ],
      "metadata": {
        "id": "Eo9ocMy6NZEs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Card Type'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nIENlIsGMedx",
        "outputId": "20783061-7541-4f41-d550-4f3b4ee081b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DIAMOND     2507\n",
              "GOLD        2502\n",
              "SILVER      2496\n",
              "PLATINUM    2495\n",
              "Name: Card Type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df['Card Type'].value_counts().index, df['Card Type'].value_counts())\n",
        "plt.xlabel('Card Type')\n",
        "plt.ylabel('Average Of Use')\n",
        "plt.scatter(df['Card Type'].value_counts().index, df['Card Type'].value_counts())\n",
        "plt.xlabel('Card Type')\n",
        "plt.ylabel('Average Of Use')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "zuXTM6taNBFZ",
        "outputId": "ad936c3a-8718-4d82-9e17-df461b0dd2d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Average customer satisfaction with the bank.**"
      ],
      "metadata": {
        "id": "LD8ekUlHN5kK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Satisfaction Score'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_qcuZYfNESs",
        "outputId": "d147626f-b88a-4d23-fd39-97de4a5168f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    2042\n",
              "2    2014\n",
              "4    2008\n",
              "5    2004\n",
              "1    1932\n",
              "Name: Satisfaction Score, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(df['Satisfaction Score'].value_counts().index, df['Satisfaction Score'].value_counts())\n",
        "plt.xlabel('Satisfaction Score')\n",
        "plt.ylabel('Values')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "uzbflvcnNdbM",
        "outputId": "6e02eaa2-a111-4d2e-ddd4-fc0c19cd77c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**I showed the number of customers who gave up using this bank in the chart. The number of '0' dropouts is the number of customers who continue to use '1'.**"
      ],
      "metadata": {
        "id": "G5jqwb-OUWbe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "exited = df['Exited'].value_counts()\n",
        "exited"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g8pYNY6STOK4",
        "outputId": "b7900cae-6804-49fa-c5a7-bbdb4b5724fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    7962\n",
              "1    2038\n",
              "Name: Exited, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar([\"0\", \"1\"], df['Exited'].value_counts())\n",
        "plt.xlabel('Station')\n",
        "plt.ylabel('Values')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "WQ2-G_NjTQed",
        "outputId": "4a46dee9-70ec-4d9e-cc12-5c5c4879765c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The reason I used strings for x values was that when I used integer or float, values such as 0.5 , 1.5 were coming apart from 0 and 1. In order to prevent this in the graph, I gave it as a string value.**"
      ],
      "metadata": {
        "id": "wpCwKtGVVHN2"
      }
    }
  ]
}