09_Logistic_Regression.ipynb

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      "name": "09 Logistic Regression.ipynb",
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        "<a href=\"https://colab.research.google.com/github/sandipanpaul21/logistic-regression-in-python/blob/main/09_Logistic_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Libraries\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import numpy as np\n",
        "from IPython.display import Image"
      ],
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      "outputs": []
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        "**Goal is to find the logistic regression function** \n",
        "- 𝑝(𝐱) such that the predicted responses 𝑝(𝐱ᵢ) are as close as possible to the actual response 𝑦ᵢ for each observation 𝑖 = 1, …, 𝑛. \n",
        "- Remember that the actual response can be only 0 or 1 in binary classification problems\n",
        "- This means that each 𝑝(𝐱ᵢ) should be close to either 0 or 1. That’s why it’s convenient to use the sigmoid function.\n",
        "\n",
        "**Logistic regression**\n",
        "- It is a linear classifier, so we’ll use a linear function 𝑓(𝐱) = 𝑏₀ + 𝑏₁𝑥₁ + ⋯ + 𝑏ᵣ𝑥ᵣ, also called the **logit**. \n",
        "- The variables 𝑏₀, 𝑏₁, …, 𝑏ᵣ are the estimators of the regression coefficients, which are also called the **predicted weights or just coefficients.**\n",
        "- The logistic regression function 𝑝(𝐱) is the **sigmoid function** of 𝑓(𝐱): 𝑝(𝐱) = 1 / (1 + exp(−𝑓(𝐱)). As such, it’s often close to either 0 or 1. \n",
        "- The function 𝑝(𝐱) is often interpreted as the **predicted probability** that the output for a given 𝐱 is equal to 1. Therefore, 1 − 𝑝(𝑥) is the probability that the output is 0.\n",
        "- Logistic regression determines the best predicted weights 𝑏₀, 𝑏₁, …, 𝑏ᵣ such that the function 𝑝(𝐱) is as close as possible to all actual responses 𝑦ᵢ, 𝑖 = 1, …, 𝑛, where 𝑛 is the number of observations. The process of calculating the best weights using available observations is called **model training or fitting**.\n",
        "- To get the best weights, you usually maximize the log-likelihood function (LLF) for all observations 𝑖 = 1, …, 𝑛. This method is called the **maximum likelihood estimation** and is represented by the equation LLF = Σᵢ(𝑦ᵢ log(𝑝(𝐱ᵢ)) + (1 − 𝑦ᵢ) log(1 − 𝑝(𝐱ᵢ))).\n",
        "- When 𝑦ᵢ = 0, the LLF for the corresponding observation is equal to log(1 − 𝑝(𝐱ᵢ)). If 𝑝(𝐱ᵢ) is close to 𝑦ᵢ = 0, then log(1 − 𝑝(𝐱ᵢ)) is close to 0. This is the result you want. If 𝑝(𝐱ᵢ) is far from 0, then log(1 − 𝑝(𝐱ᵢ)) drops significantly. You don’t want that result because your goal is to obtain the maximum LLF. Similarly, when 𝑦ᵢ = 1, the LLF for that observation is 𝑦ᵢ log(𝑝(𝐱ᵢ)). If 𝑝(𝐱ᵢ) is close to 𝑦ᵢ = 1, then log(𝑝(𝐱ᵢ)) is close to 0. If 𝑝(𝐱ᵢ) is far from 1, then log(𝑝(𝐱ᵢ)) is a large negative number.\n",
        "- Once you determine the best weights that define the function 𝑝(𝐱), you can get the predicted outputs 𝑝(𝐱ᵢ) for any given input 𝐱ᵢ. For each observation 𝑖 = 1, …, 𝑛, the predicted output is 1 if 𝑝(𝐱ᵢ) > 0.5 and 0 otherwise. The threshold doesn’t have to be 0.5, but it usually is. You might define a lower or higher value if that’s more convenient for your situation.\n",
        "- There’s one more important relationship between 𝑝(𝐱) and 𝑓(𝐱), which is that log(𝑝(𝐱) / (1 − 𝑝(𝐱))) = 𝑓(𝐱). This equality explains why 𝑓(𝐱) is the logit. It implies that 𝑝(𝐱) = 0.5 when 𝑓(𝐱) = 0 and that the predicted output is 1 if 𝑓(𝐱) > 0 and 0 otherwise.\n",
        "\n",
        "**Type of Logistic Regression**\n",
        "1. **Single-Variate Logistic Regression**\n",
        "  - Below is an example of Single Variate Logistic Regression\n",
        "  - There is only one independent variable (or feature), which is 𝐱 = 𝑥. \n",
        "  - Here, you have a given set of input-output (or 𝑥-𝑦) pairs, represented by green circles. These are your observations. Remember that 𝑦 can only be 0 or 1. For example, the leftmost green circle has the input 𝑥 = 0 and the actual output 𝑦 = 0. The rightmost observation has 𝑥 = 9 and 𝑦 = 1.\n",
        "  - Logistic regression finds the weights 𝑏₀ and 𝑏₁ that correspond to the maximum LLF. \n",
        "  - These weights define the logit 𝑓(𝑥) = 𝑏₀ + 𝑏₁𝑥, which is the dashed black line. \n",
        "  - They also define the predicted probability 𝑝(𝑥) = 1 / (1 + exp(−𝑓(𝑥))), shown here as the full black line. \n",
        "  - In this case, the threshold 𝑝(𝑥) = 0.5 and 𝑓(𝑥) = 0 corresponds to the value of 𝑥 slightly higher than 3. This value is the limit between the inputs with the predicted outputs of 0 and 1."
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "  2. **Multi-Variate Logistic Regression**\n",
        "    - Multi-variate logistic regression has more than one input variable. This figure shows the classification with two independent variables, 𝑥₁ and 𝑥₂.\n",
        "    - The graph is different from the single-variate graph because both axes represent the inputs. The outputs also differ in color. The white circles show the observations classified as zeros, while the green circles are those classified as ones.\n",
        "  - Logistic regression determines the weights 𝑏₀, 𝑏₁, and 𝑏₂ that maximize the LLF. Once you have 𝑏₀, 𝑏₁, and 𝑏₂, you can get:\n",
        "    - The logit 𝑓(𝑥₁, 𝑥₂) = 𝑏₀ + 𝑏₁𝑥₁ + 𝑏₂𝑥₂\n",
        "    - The probabilities 𝑝(𝑥₁, 𝑥₂) = 1 / (1 + exp(−𝑓(𝑥₁, 𝑥₂)))\n",
        "  - The dash-dotted black line linearly separates the two classes. This line corresponds to 𝑝(𝑥₁, 𝑥₂) = 0.5 and 𝑓(𝑥₁, 𝑥₂) = 0."
      ],
      "metadata": {
        "id": "VPmBff7QvTuq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Image('/content/drive/MyDrive/Demo Images/File_702.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "LrjJ5GDTvTQp",
        "outputId": "d9097bc8-15be-4993-bb2b-42e5af931e31"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Regularization**\n",
        "- **Overfitting** is one of the most serious kinds of problems related to machine learning. It occurs when a model learns the training data too well. The model then learns not only the relationships among data but also the noise in the dataset. \n",
        "- Overfitted models tend to have good performance with the data used to fit them (the training data), but they behave poorly with unseen data (or test data, which is data not used to fit the model).\n",
        "- Overfitting usually occurs with complex models. \n",
        "- **Regularization** normally tries to reduce or penalize the complexity of the model. Regularization techniques applied with logistic regression mostly tend to penalize large coefficients 𝑏₀, 𝑏₁, …, 𝑏ᵣ:\n",
        "  1. *L1 regularization* penalizes the LLF with the scaled sum of the absolute values of the weights: |𝑏₀|+|𝑏₁|+⋯+|𝑏ᵣ|.\n",
        "  2. *L2 regularization* penalizes the LLF with the scaled sum of the squares of the weights: 𝑏₀²+𝑏₁²+⋯+𝑏ᵣ².\n",
        "  3. *Elastic-net regularization* is a linear combination of L1 and L2 regularization.\n",
        "Regularization can significantly improve model performance on unseen data.\n",
        "\n"
      ],
      "metadata": {
        "id": "zfbfQtFyv4PF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "## Loading data directly from UCLA\n",
        "df = pd.read_stata(\"https://stats.idre.ucla.edu/stat/stata/dae/binary.dta\")\n",
        "\n",
        "## Loading data from our GitHub\n",
        "df2 = pd.read_csv(\"https://raw.githubusercontent.com/Opensourcefordatascience/Data-sets/master/admission.csv\")\n",
        "\n",
        "## Taking a look at the data\n",
        "df.describe()\n",
        "\n",
        "# Inference : \n",
        "# Looking at the descriptive statistics, \n",
        "# 31% of the students get admitted to a graduate program, \n",
        "# the average GRE score is 587 with a large standard deviation, \n",
        "# the average GPA is 3.39\n",
        "# the average undergraduate school prestige is 2.49."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "u0xfA13ow6r6",
        "outputId": "6f642de0-0f2b-464b-8fdb-8e2cf3022312"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>admit</th>\n",
              "      <th>gre</th>\n",
              "      <th>gpa</th>\n",
              "      <th>rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.317500</td>\n",
              "      <td>587.700012</td>\n",
              "      <td>3.389901</td>\n",
              "      <td>2.485000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.466087</td>\n",
              "      <td>115.516663</td>\n",
              "      <td>0.380567</td>\n",
              "      <td>0.944462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>220.000000</td>\n",
              "      <td>2.260000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>520.000000</td>\n",
              "      <td>3.130000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>580.000000</td>\n",
              "      <td>3.395000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>660.000000</td>\n",
              "      <td>3.670000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>800.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            admit         gre         gpa        rank\n",
              "count  400.000000  400.000000  400.000000  400.000000\n",
              "mean     0.317500  587.700012    3.389901    2.485000\n",
              "std      0.466087  115.516663    0.380567    0.944462\n",
              "min      0.000000  220.000000    2.260000    1.000000\n",
              "25%      0.000000  520.000000    3.130000    2.000000\n",
              "50%      0.000000  580.000000    3.395000    2.000000\n",
              "75%      1.000000  660.000000    3.670000    3.000000\n",
              "max      1.000000  800.000000    4.000000    4.000000"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# One thing to keep in mind, \n",
        "# the variable “rank” is truly categorical \n",
        "\n",
        "## Converting variable to categorical data type (since that what it is)\n",
        "## and then creating dummy variables\n",
        "df['rank'] = df['rank'].astype('category')\n",
        "\n",
        "#df = pd.get_dummies(df)"
      ],
      "metadata": {
        "id": "6hf1KbMAw7l8"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Needed to run the logistic regression\n",
        "import statsmodels.formula.api as smf\n",
        "\n",
        "# For plotting/checking assumptions\n",
        "import seaborn as sns\n",
        "\n",
        "# Assumption 1\n",
        "# Continuous IVs being Linearly Related to the Log Odds\n",
        "# Logistic regression does not require the continuous IV(s) to be linearly related to the DV. \n",
        "# It does require the continuous IV(s) be linearly related to the log odds of the IV though. \n",
        "# A way to test this is to plot the IV(s) in question and look for an S-shaped curve. \n",
        "# Sometimes the S-shape will not be obvious. \n",
        "# The plot should have a flat or flat-ish top and bottom with an increase or decreasing middle.\n",
        "\n",
        "gre = sns.regplot(x= 'gre', y= 'admit', data= df, logistic= True).set_title(\"GRE Log Odds Linear Plot\")\n",
        "gre"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "ljg9_FkRxed5",
        "outputId": "27165891-5eac-4a22-f839-f8135d0571f2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'GRE Log Odds Linear Plot')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gpa = sns.regplot(x= 'gpa', y= 'admit', data= df, logistic= True).set_title(\"GPA Log Odds Linear Plot\")\n",
        "gpa\n",
        "\n",
        "# Inference : \n",
        "# It may be hard to see, but the data does have somewhat of a curve occurring \n",
        "# that resembles the S-shaped curve that is required. \n",
        "# If a non-S-shaped line were to be present, sometimes a U-shape will be present, \n",
        "# how to handle that data needs to be considered."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "butvO5kqyQjY",
        "outputId": "00b7d14f-fb1d-4b0b-961d-007ea600ddf9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'GPA Log Odds Linear Plot')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assumption 2\n",
        "# Absence of Multicollinearity\n",
        "# An easy way to test this is to use a correlation matrix,\n",
        "# and look for any highly correlated variables, \n",
        "# and/or to look at for high Variance Inflation Factor (VIF) scores. \n",
        "# If there are variables that are highly correlated, or have a high VIF, \n",
        "# a corrective action would be to drop one of them since they are measuring the same/similar thing.\n",
        "\n",
        "df.corr()\n",
        "\n",
        "# Inference : \n",
        "# No Multicollinearity"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "inQZvyTcyeSl",
        "outputId": "9adc8ee9-c55b-44a9-9b81-e76fa0d341a0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>admit</th>\n",
              "      <th>gre</th>\n",
              "      <th>gpa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>admit</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.184434</td>\n",
              "      <td>0.178212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gre</th>\n",
              "      <td>0.184434</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.384266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gpa</th>\n",
              "      <td>0.178212</td>\n",
              "      <td>0.384266</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          admit       gre       gpa\n",
              "admit  1.000000  0.184434  0.178212\n",
              "gre    0.184434  1.000000  0.384266\n",
              "gpa    0.178212  0.384266  1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assumption 3\n",
        "# Lack of outliers\n",
        "# The assumption of lack of outliers is an easy one to check. \n",
        "# One can get a feel of this with the descriptive statistics provided by the .describe() method. \n",
        "# The easiest way to check for outliers is to use a box plot.\n",
        "# Do to there being a drastic difference between the values used to measure GRE and the GPA/ Rank,\n",
        "# two separate box plot charts will be produced.\n",
        "\n",
        "gpa_rank_box = sns.boxplot(data= df[['gpa', 'rank']]).set_title(\"GPA and Rank Box Plot\")\n",
        "gpa_rank_box"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "Qv0g4jHQypZd",
        "outputId": "e1ad41ad-6b3d-4b6e-820b-42b680c0e866"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'GPA and Rank Box Plot')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gre_box = sns.boxplot(x= 'gre', data= df, orient= 'v').set_title(\"GRE Box Plot\")\n",
        "gre_box\n",
        "\n",
        "# Inference : \n",
        "# There looks to be 2 values that could be considered outliers for the GRE variable, \n",
        "# and 1 value for GPA variable. \n",
        "# In both of these cases, the values that are in question are not so far away from the rest \n",
        "# of the values in their respective variable. \n",
        "# Thus, the values can be kept and used in the analysis."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "KTQ5XkblzAtb",
        "outputId": "b3134bf2-7d36-4eba-c495-5495c4d0f20b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'GRE Box Plot')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Logistic Regression Model**\n",
        "\n",
        "- The assumptions have been checked, and the data is good to run.\n",
        "- For the “Rank” variable, one can either use the dummy variables created to look at multicollinearity or by using the “C(variable_of_interest)”. \n",
        "- If using the dummy variables, be sure to not include 1 of the groups to avoid the Dummy Variable Trap. The group dropped is then considered the reference group for the other dummy variables that came from the same original variable. Most commonly, the highest ranked group is dropped, or sometimes the lowest ranked group is dropped – it all depends on the hypothesis. \n",
        "- Using the “C(variable_of_interest)” method automatically does this.\n",
        "\n",
        "**Recap**\n",
        "- To recap, the analysis is looking into the effects GRE score, GPA, and undergraduate university prestige has on admission into the program. \n",
        "- Using the api formula method, the general structure is as follows smf.logit(formula=\" DV ~ IV1 + IV2 + IVn, data= your_data_frame)."
      ],
      "metadata": {
        "id": "-zI5hDfTzqPq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model= smf.logit(formula=\"admit~ gre + gpa + C(rank)\", data= df).fit()\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "vrAICqq2ziUY",
        "outputId": "5829b9f6-c954-4bc9-cbc8-e03845ca9b75"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimization terminated successfully.\n",
            "         Current function value: 0.573147\n",
            "         Iterations 6\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Logit Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>         <td>admit</td>      <th>  No. Observations:  </th>  <td>   400</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   394</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     5</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>            <td>Mon, 13 Dec 2021</td> <th>  Pseudo R-squ.:     </th>  <td>0.08292</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                <td>12:53:22</td>     <th>  Log-Likelihood:    </th> <td> -229.26</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -249.99</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>7.578e-08</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th>      <td>   -3.9900</td> <td>    1.140</td> <td>   -3.500</td> <td> 0.000</td> <td>   -6.224</td> <td>   -1.756</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(rank)[T.2.0]</th> <td>   -0.6754</td> <td>    0.316</td> <td>   -2.134</td> <td> 0.033</td> <td>   -1.296</td> <td>   -0.055</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(rank)[T.3.0]</th> <td>   -1.3402</td> <td>    0.345</td> <td>   -3.881</td> <td> 0.000</td> <td>   -2.017</td> <td>   -0.663</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(rank)[T.4.0]</th> <td>   -1.5515</td> <td>    0.418</td> <td>   -3.713</td> <td> 0.000</td> <td>   -2.370</td> <td>   -0.733</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>gre</th>            <td>    0.0023</td> <td>    0.001</td> <td>    2.070</td> <td> 0.038</td> <td>    0.000</td> <td>    0.004</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>gpa</th>            <td>    0.8040</td> <td>    0.332</td> <td>    2.423</td> <td> 0.015</td> <td>    0.154</td> <td>    1.454</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                           Logit Regression Results                           \n",
              "==============================================================================\n",
              "Dep. Variable:                  admit   No. Observations:                  400\n",
              "Model:                          Logit   Df Residuals:                      394\n",
              "Method:                           MLE   Df Model:                            5\n",
              "Date:                Mon, 13 Dec 2021   Pseudo R-squ.:                 0.08292\n",
              "Time:                        12:53:22   Log-Likelihood:                -229.26\n",
              "converged:                       True   LL-Null:                       -249.99\n",
              "Covariance Type:            nonrobust   LLR p-value:                 7.578e-08\n",
              "==================================================================================\n",
              "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
              "----------------------------------------------------------------------------------\n",
              "Intercept         -3.9900      1.140     -3.500      0.000      -6.224      -1.756\n",
              "C(rank)[T.2.0]    -0.6754      0.316     -2.134      0.033      -1.296      -0.055\n",
              "C(rank)[T.3.0]    -1.3402      0.345     -3.881      0.000      -2.017      -0.663\n",
              "C(rank)[T.4.0]    -1.5515      0.418     -3.713      0.000      -2.370      -0.733\n",
              "gre                0.0023      0.001      2.070      0.038       0.000       0.004\n",
              "gpa                0.8040      0.332      2.423      0.015       0.154       1.454\n",
              "==================================================================================\n",
              "\"\"\""
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**INTERPRETING LOGISTIC REGRESSION**\n",
        "\n",
        "- The model summary shows the coefficients, standard error, the associated z-score, and the 95% confidence intervals. \n",
        "- We interpret the results as follows, the overall model is significant indicated by a LLR p-value < 0.05 (7.578e-08) which allows us to look at the rest of the results. \n",
        "- All the IVs have a significant effect on the log odds of being admitted as indicated by the z values < 0.05.\n",
        "\n",
        "**Numeric Variables**\n",
        "\n",
        "- Interpreting continuous variables is not very different from interpreting them in a linear regression model. \n",
        "- For every one unit increase in gre score, the log odds of admission increase by 0.0023; for every one unit increase in gpa, the log odds of admission increase by 0.8040.\n",
        "\n",
        "**Categorical Variables**\n",
        "\n",
        "- The categorical variables have a different interpretation. \n",
        "- Since Rank 1 was dropped from the analysis, it is the comparison group and plays an important role in interpreting the other categories.\n",
        "- For example, if an applicant attended a Rank 2 University compared to a Rank 1 University, there is a -0.6754 decrease in the log odds of admission; if an applicant attended a Rank 3 University compared to a Rank 1 University, there is a -1.3402 decrease in the log odds of admission.\n",
        "\n",
        "**TAKING LOGISTIC REGRESSION A STEP FURTHER**\n",
        "\n",
        "- Interpreting the log odds is not very straight forward when thinking about it’s effects. \n",
        "- An easier way to interpret the findings is by converting the coefficients of the logistic regression model into odd ratios. \n",
        "- This can be done by getting the exponent of the coefficient value."
      ],
      "metadata": {
        "id": "EeFQIn7f0Hwp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# GETTING THE ODDS RATIOS, Z-VALUE, AND 95% CI\n",
        "model_odds = pd.DataFrame(np.exp(model.params), columns= ['OR'])\n",
        "model_odds['z-value']= model.pvalues\n",
        "model_odds[['2.5%', '97.5%']] = np.exp(model.conf_int())\n",
        "model_odds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "5J7tk6km0G0e",
        "outputId": "d893c160-8704-4b12-97fe-db75b46107f5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>OR</th>\n",
              "      <th>z-value</th>\n",
              "      <th>2.5%</th>\n",
              "      <th>97.5%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Intercept</th>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.000465</td>\n",
              "      <td>0.001981</td>\n",
              "      <td>0.172783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C(rank)[T.2.0]</th>\n",
              "      <td>0.508931</td>\n",
              "      <td>0.032829</td>\n",
              "      <td>0.273692</td>\n",
              "      <td>0.946358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C(rank)[T.3.0]</th>\n",
              "      <td>0.261792</td>\n",
              "      <td>0.000104</td>\n",
              "      <td>0.133055</td>\n",
              "      <td>0.515089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C(rank)[T.4.0]</th>\n",
              "      <td>0.211938</td>\n",
              "      <td>0.000205</td>\n",
              "      <td>0.093443</td>\n",
              "      <td>0.480692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gre</th>\n",
              "      <td>1.002267</td>\n",
              "      <td>0.038465</td>\n",
              "      <td>1.000120</td>\n",
              "      <td>1.004418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gpa</th>\n",
              "      <td>2.234545</td>\n",
              "      <td>0.015388</td>\n",
              "      <td>1.166122</td>\n",
              "      <td>4.281877</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      OR   z-value      2.5%     97.5%\n",
              "Intercept       0.018500  0.000465  0.001981  0.172783\n",
              "C(rank)[T.2.0]  0.508931  0.032829  0.273692  0.946358\n",
              "C(rank)[T.3.0]  0.261792  0.000104  0.133055  0.515089\n",
              "C(rank)[T.4.0]  0.211938  0.000205  0.093443  0.480692\n",
              "gre             1.002267  0.038465  1.000120  1.004418\n",
              "gpa             2.234545  0.015388  1.166122  4.281877"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Now the interpretation is easier.** \n",
        "- Converting the logistic coefficients into odds ratios makes it easier to interpret the effects on the DV. \n",
        "- The confidence intervals have been converted to odds as well.\n",
        "\n",
        "**Numeric Variables**\n",
        "- For every one unit increase in gpa, the odds of being admitted increases by a factor of 2.235; for every one unit increase in gre score, the odds of being admitted increases by a factor of 1.002.\n",
        "\n",
        "**Categorical Variables**\n",
        "- Still interpreting the results in comparison to the group that was dropped. \n",
        "- Applicants from a Rank 2 University compared to a Rank 1 University are 0.509 as likely to be admitted; applicants from a Rank 3 University compared to a Rank 1 University are 0.262 as likely to be admitted, etc."
      ],
      "metadata": {
        "id": "mPJ-Y6dd0izX"
      }
    }
  ]
}