project 69 data_analyst_example.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "example :\n",
        "When predicting the virality of a new hashtag, you might use a classification model and calculate the confidence interval for the AUC score to quantify uncertainty."
      ],
      "metadata": {
        "id": "ypitPBAiC_I7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.datasets import make_classification\n",
        "\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "FSe28Z0tDDKm"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "we will generate an example dataset after importing the libraries for analysis."
      ],
      "metadata": {
        "id": "-TEoPUffDpRb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = make_classification(n_samples = 1000, n_features = 20, n_classes = 2, random_state = 42)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "x-pjxcEJDzOw"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "we will use random forest for classification"
      ],
      "metadata": {
        "id": "yU8TX3RXEg2a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(random_state = 42)"
      ],
      "metadata": {
        "id": "0ZIvjWbhEkIB"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit (X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "o_LsyNkZEvDk",
        "outputId": "722b5597-986a-4ea3-9e78-d155bd40918f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making the predictions"
      ],
      "metadata": {
        "id": "c1EIlri7E07b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_probs = model.predict_proba(X_test)[:, 1]\n",
        "\n",
        "# predicting the probabilities of the positive class"
      ],
      "metadata": {
        "id": "RwB73mqzEzyB"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "calculating the auc score"
      ],
      "metadata": {
        "id": "OA_krU0zFFdW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "auc_score = roc_auc_score(y_test, y_probs)\n",
        "auc_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_0WEe0SFIqR",
        "outputId": "07c10c96-13bb-454a-b6c9-cb5a57c6cff4"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9379459350819013"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "An auc with 0.93 says that the model is almost 93% giving correct predictions.\n",
        "An auc score should be between 0 and 1 with 0 as 100% false predictions 1 being 100 correct predictions."
      ],
      "metadata": {
        "id": "2_QIXXeYGTei"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# adjusting the functions to return bootstrapped AUC scores for visualization\n",
        "\n",
        "def bootstrap_auc(y_true, y_probs, n_bootstraps = 1000, random_state = 42):\n",
        "  rng = np.random.RandomState(random_state)\n",
        "  bootstrapped_scores = []\n",
        "\n",
        "  for i in range(n_bootstraps):\n",
        "    indices = rng.randint(0, len(y_probs), len(y_probs))\n",
        "\n",
        "    if len(np.unique(y_true[indices])) < 2:\n",
        "      continue\n",
        "\n",
        "    score = roc_auc_score(y_true[indices], y_probs[indices])\n",
        "    bootstrapped_scores.append(score)\n",
        "\n",
        "  sorted_scores = np.array(bootstrapped_scores)\n",
        "  confidence_lower = np.percentile(sorted_scores, 2.5)\n",
        "  confidence_upper = np.percentile(sorted_scores, 97.5)\n",
        "\n",
        "  return bootstrapped_scores, confidence_lower, confidence_upper"
      ],
      "metadata": {
        "id": "gPrxhpTIGnn3"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conf_int_lower, conf_int_upper, bootstrapped_scores = bootstrap_auc(y_test, y_probs)"
      ],
      "metadata": {
        "id": "nbWH7aP2ICoY"
      },
      "execution_count": 17,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "conf_int_lower"
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conf_int_upper"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i0XSfVBuItND",
        "outputId": "ee8f6d49-2563-4e44-a356-23fece9e00de"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9028613244107196"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bootstrapped_scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDNvJPKlItQj",
        "outputId": "a2289be8-5380-48b9-97c8-63ebb71afd47"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9669993305130694"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the mean of the confidence intervals to represent them as single lines\n",
        "mean_conf_int_lower = np.mean(conf_int_lower)\n",
        "mean_conf_int_upper = np.mean(conf_int_upper)\n",
        "\n",
        "plt.hist(bootstrapped_scores, bins=50, alpha=0.75)\n",
        "plt.axvline(auc_score, color='red', linestyle='--', label='AUC Score')\n",
        "\n",
        "# Use the mean values for plotting the vertical lines\n",
        "plt.axvline(mean_conf_int_lower, color='green', linestyle='--', label='2.5th percentile')\n",
        "plt.axvline(mean_conf_int_upper, color='green', linestyle='--', label='97.5th percentile')\n",
        "plt.title('Distribution of Bootstrapped AUC Scores')\n",
        "plt.xlabel('AUC Score')\n",
        "plt.ylabel('Frequency')\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "HP2P6R45JnAX",
        "outputId": "bc8080ed-9a98-4bf7-8756-151075ea5784"
      },
      "execution_count": 29,
      "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 output histogram displays the bootstrapped AUC scores as a distribution, showcasing the variability in the AUC score that might arise due to the random sampling of the data. The red dashed line marks the AUC score calculated from the test set, while the green dashed lines mark the lower and upper bounds of the 95% confidence interval."
      ],
      "metadata": {
        "id": "rAxRsvKZJzVr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To find if longer tweets have high engagement scores ?"
      ],
      "metadata": {
        "id": "u-n3nHikJ7nk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats\n",
        "\n",
        "tweet_lengths = np.random.randint(20, 280, 100)\n",
        "engagement_scores = tweet_lengths * 0.5 + np.random.normal(0, 10, 100)"
      ],
      "metadata": {
        "id": "POHSUWj3KAWP"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corr_coefficient, _ = stats.pearsonr(tweet_lengths, engagement_scores)\n",
        "\n",
        "print(f\"Pearson Correlation Coefficient: {corr_coefficient:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bIu24SJMKHek",
        "outputId": "c9d64120-d8a4-4858-87ce-2da54cf6ba17"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pearson Correlation Coefficient: 0.958\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Pearson correlation coefficient (r) is the most common way of measuring a linear correlation. It is a number between -1 and 1 that measures the strength and direction of the relationship between two variables.\n",
        "\n",
        "- High Degree: Values between ±0.50 and ±1 suggest a strong correlation.\n",
        "\n",
        "- Moderate Degree: Values between ±0.30 and ±0.49 indicate a moderate correlation.\n",
        "\n",
        "- Low Degree: Values below +0.29 are considered a weak correlation. No Correlation: A value of zero implies no relationship."
      ],
      "metadata": {
        "id": "AxlM0BdHKQzP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Performing a two-sample t-test to compare engagement scores between two groups"
      ],
      "metadata": {
        "id": "jGkyPfPYKfCw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import ttest_ind\n",
        "\n",
        "# Simulated engagement scores for A/B groups\n",
        "group_a = np.random.normal(200, 50, 100)\n",
        "group_b = np.random.normal(205, 50, 100)"
      ],
      "metadata": {
        "id": "3upglAMNKbhG"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t_stat, p_value = ttest_ind(group_a, group_b)\n",
        "\n",
        "print(f\"T-statistic: {t_stat:.3f}, P-value: {p_value:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L11A5DflKpbU",
        "outputId": "9a006a03-e059-4693-d3e6-ca7361b0d286"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-statistic: -0.782, P-value: 0.4354\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The t-test results in a T-statistics, indicate no statistically significant difference between the groups."
      ],
      "metadata": {
        "id": "-IDdjk7vKudV"
      }
    }
  ]
}