diff --git a/Explainable-AI/Intro_to_LIME_and_Shap/Readme.md b/Explainable-AI/Intro_to_LIME_and_Shap/Readme.md new file mode 100644 index 0000000000..5472ac90e8 --- /dev/null +++ b/Explainable-AI/Intro_to_LIME_and_Shap/Readme.md @@ -0,0 +1,198 @@ +# Explainable AI: Using Local Interpretable Model-agnostic Explanations (LIME) & SHapley Additive exPlanations (SHAP) + +As reliance on Artificial Intelligence (AI) increases, the explainability of AI systems remains a significant concern. Often described as "black boxes," AI solutions can be challenging to interpret. To address this, Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are two frameworks designed to enhance our understanding of model behavior. This notebook explores these frameworks and their potential use cases. + +## Setting Up + +We begin by importing the necessary libraries: + +- **NumPy**: for numerical data manipulation +- **Pandas**: for data handling and analysis +- **Matplotlib**: for creating static visualizations +- **Seaborn**: for enhanced data visualizations + +```python +# Importing the libraries +import numpy as np +import pandas as pd +from matplotlib import pyplot as plt +import seaborn as sns +``` + +Next, we import the Wine Quality Dataset, a widely recognized classification dataset, using the `read_csv()` function from Pandas. + +```python +# Import the dataset +df = pd.read_csv('../input/wine-dataset/wine.csv') + +# Viewing the first few rows +df.head() +``` + +## Preprocessing the Dataset + +To start, we check the dimensions of the dataset using the `shape` attribute. + +```python +# Checking the dimensions of the dataframe +df.shape +``` + +This reveals that the dataset consists of 178 rows and 14 columns. + +Next, we check for missing values using the `isnull()` and `sum()` functions. + +```python +# Checking for missing values +df.isnull().sum() +``` + +We find that there are no missing values. + +We then examine the data types to determine if any categorical features require encoding. + +```python +# Checking for encoding categorical features +df.dtypes +``` + +With no categorical values present, no encoding is necessary. + +Next, we investigate outliers by creating a boxplot of all attributes using the `boxplot()` function. + +```python +# Checking for outliers +plt.figure(figsize=(15, 8)) +df.boxplot() +plt.title('Boxplot of the Dataset') +plt.xlabel('Attributes') +plt.ylabel('Values') +``` + +This boxplot indicates a negligible number of outliers. + +We then plot the distribution of each column using `kdeplot()` from the Seaborn library to visualize the distribution of the data. + +```python +# Plotting the distribution of Alcohol content +plt.figure() +sns.kdeplot(df['Alcohol']) +plt.title('Distribution of variable - Alcohol') +plt.xlabel('Values of Alcohol Content') +``` + +Subsequent distribution plots for other attributes reveal various shapes and skewnesses, confirming that while some distributions are normal, others exhibit peaks or skewness. Given the limited size of the dataset, we will not perform transformations and will not scale the data, as we intend to build a non-parametric model. + +## Model Building + +In this notebook, we will build a Random Forest model for classification. We start by splitting the data into independent variables (X) and the dependent variable (y) using the `train_test_split()` function from the `sklearn` library. + +```python +# Splitting the data into independent and dependent variables +X = df.drop(columns=['Class']) +y = df['Class'] + +# Dividing the dataset into training and testing sets +from sklearn.model_selection import train_test_split +X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, random_state=105) +``` + +Next, we build the model using the `RandomForestClassifier()` from `sklearn`. + +```python +# Building the model +from sklearn.ensemble import RandomForestClassifier +model = RandomForestClassifier(random_state=105) +``` + +To optimize the model, we create a dictionary of parameters and their possible values for optimization. We then employ a grid search to identify the best parameter combination with 10-fold cross-validation. + +```python +# Importing the necessary libraries +from sklearn.model_selection import GridSearchCV + +# Creating a dictionary and list of their values to optimize the model +params = { + 'n_estimators': [100, 500, 1000], + 'criterion': ['gini', 'entropy'], + 'max_depth': [3, 4, 5, 6, 7, 8, 9, 10], +} + +# Initiating a grid search to find the most optimum parameters +grid_search = GridSearchCV(model, params, cv=10) + +# Fitting the training data +grid_search.fit(X_train, y_train) +``` + +Next, we retrieve the best estimator from the grid search and fit the training data to this optimized model. We then generate a classification report to evaluate the model's performance. + +```python +# Obtaining the best model +model = grid_search.best_estimator_ + +# Fitting the training data +model.fit(X_train, y_train) + +# Obtaining the classification report +from sklearn.metrics import classification_report +print(classification_report(y_test, model.predict(X_test))) +``` + +With our model built and evaluated, we can now analyze its performance. + +## Explainability via LIME + +Local Interpretable Model-agnostic Explanations (LIME) allows us to interpret individual predictions made by the model. To start, we import the `lime` library and its `lime_tabular` module. + +```python +# Importing LIME +import lime +from lime import lime_tabular +``` + +We then create an instance of the `LimeTabularExplainer` class, passing in our training data, feature names, class names, and setting the mode to 'classification'. + +```python +# Creating an instance of the lime tabular explainer +lime_explainer = lime_tabular.LimeTabularExplainer( + training_data=np.array(X_train), + feature_names=X_train.columns, + class_names=['1', '2', '3'], + mode='classification' +) +``` + +Next, we derive the explanation for a specific instance using the `explain_instance` method. The parameters include the data row to explain, the prediction function, the number of top labels to display, and the number of features to consider. + +```python +# Obtaining the explanation +explanation = lime_explainer.explain_instance( + data_row=X_test.iloc[1], + predict_fn=model.predict_proba, + top_labels=6, + num_features=13 +) + +# Printing out the explanation +explanation.show_in_notebook() +``` + +The output reveals how the model predicts class membership with associated confidence levels for each class. Each attribute's weight indicates its contribution to the prediction, providing valuable insight into the model's decision-making process. + +## Explainability via SHAP + +SHapley Additive exPlanations (SHAP) offers another approach for interpreting model predictions by analyzing the contribution of each feature to the overall prediction. This framework uses game theory to assess the impact of features systematically. + +### Next Steps with SHAP + +To implement SHAP, we would: + +1. Install the SHAP library. +2. Import the necessary modules. +3. Create SHAP values using the trained model. +4. Visualize the SHAP values to understand feature importance and model behavior. + +This approach allows for a deeper understanding of how each feature influences the model's predictions, making AI systems more interpretable and trustworthy. + +By employing both LIME and SHAP, we can significantly reduce the "black box" nature of AI models, allowing stakeholders to understand, trust, and utilize these powerful tools more effectively. diff --git a/Explainable-AI/Intro_to_LIME_and_Shap/explainable-ai-intro-to-lime-shap.ipynb b/Explainable-AI/Intro_to_LIME_and_Shap/explainable-ai-intro-to-lime-shap.ipynb new file mode 100644 index 0000000000..3699ac3ea1 --- /dev/null +++ b/Explainable-AI/Intro_to_LIME_and_Shap/explainable-ai-intro-to-lime-shap.ipynb @@ -0,0 +1,39611 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "436f1b44", + "metadata": { + "papermill": { + "duration": 0.017658, + "end_time": "2022-08-22T10:49:16.857201", + "exception": false, + "start_time": "2022-08-22T10:49:16.839543", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Explainable AI: Using Local Interpretable Model-agnostic Explanations (LIME) & SHapley Additive exPlanations (SHAP)" + ] + }, + { + "cell_type": "markdown", + "id": "d85909ea", + "metadata": { + "papermill": { + "duration": 0.017923, + "end_time": "2022-08-22T10:49:16.891506", + "exception": false, + "start_time": "2022-08-22T10:49:16.873583", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Humans are becoming more and more dependent on Artificial Intelligence. However, the explainability of AI systems is still a subject of debate, and AI Solutions are referred to as 'Black Boxes'. \n", + "\n", + "Lcoal Interpretable Model-agnostic Explanations (LIME) & SHapley Additive exPlanations (SHAP) are two frameworks which can help us understand the working of models to some extent. In this notebook, we will explore these two frameworks and the use-cases where they can be used." + ] + }, + { + "cell_type": "markdown", + "id": "b4f5658e", + "metadata": { + "papermill": { + "duration": 0.016671, + "end_time": "2022-08-22T10:49:16.925501", + "exception": false, + "start_time": "2022-08-22T10:49:16.908830", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Setting Up" + ] + }, + { + "cell_type": "markdown", + "id": "5bf25077", + "metadata": { + "papermill": { + "duration": 0.016631, + "end_time": "2022-08-22T10:49:16.959514", + "exception": false, + "start_time": "2022-08-22T10:49:16.942883", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "To set up, we import the following libraries:\n", + "\n", + "- NumPy: for data manipulation\n", + "- Pandas: for data manipulation\n", + "- Matplotlib: for data visualization\n", + "- Seaborn: for data visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0f3b07f4", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:16.994889Z", + "iopub.status.busy": "2022-08-22T10:49:16.993640Z", + "iopub.status.idle": "2022-08-22T10:49:18.210933Z", + "shell.execute_reply": "2022-08-22T10:49:18.210034Z" + }, + "papermill": { + "duration": 1.238656, + "end_time": "2022-08-22T10:49:18.213945", + "exception": false, + "start_time": "2022-08-22T10:49:16.975289", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# importing the libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "id": "136b9511", + "metadata": { + "papermill": { + "duration": 0.014901, + "end_time": "2022-08-22T10:49:18.245059", + "exception": false, + "start_time": "2022-08-22T10:49:18.230158", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Next, we import a dataset to make predictions on. In this notebook, we use a well-known classification dataset - the Wine Quality Dataset to make things easier to interpret. To import the dataset, we use the read_csv() function from the Pandas library." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "71d76edd", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:18.278028Z", + "iopub.status.busy": "2022-08-22T10:49:18.276666Z", + "iopub.status.idle": "2022-08-22T10:49:18.297284Z", + "shell.execute_reply": "2022-08-22T10:49:18.296185Z" + }, + "papermill": { + "duration": 0.040123, + "end_time": "2022-08-22T10:49:18.300149", + "exception": false, + "start_time": "2022-08-22T10:49:18.260026", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# import the dataset\n", + "df = pd.read_csv('../input/wine-dataset/wine.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "61439aa8", + "metadata": { + "papermill": { + "duration": 0.015436, + "end_time": "2022-08-22T10:49:18.332200", + "exception": false, + "start_time": "2022-08-22T10:49:18.316764", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "To get a glimpse of the dataset, we view its first few rows using the head() function from the Pandas library." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d3a55ec8", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:18.367103Z", + "iopub.status.busy": "2022-08-22T10:49:18.366708Z", + "iopub.status.idle": "2022-08-22T10:49:18.395789Z", + "shell.execute_reply": "2022-08-22T10:49:18.394670Z" + }, + "papermill": { + "duration": 0.049881, + "end_time": "2022-08-22T10:49:18.398617", + "exception": false, + "start_time": "2022-08-22T10:49:18.348736", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Class Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n", + "0 1 14.23 1.71 2.43 15.6 127 \n", + "1 1 13.20 1.78 2.14 11.2 100 \n", + "2 1 13.16 2.36 2.67 18.6 101 \n", + "3 1 14.37 1.95 2.50 16.8 113 \n", + "4 1 13.24 2.59 2.87 21.0 118 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "0 2.80 3.06 0.28 2.29 \n", + "1 2.65 2.76 0.26 1.28 \n", + "2 2.80 3.24 0.30 2.81 \n", + "3 3.85 3.49 0.24 2.18 \n", + "4 2.80 2.69 0.39 1.82 \n", + "\n", + " Color intensity Hue OD280/OD315 of diluted wines Proline \n", + "0 5.64 1.04 3.92 1065 \n", + "1 4.38 1.05 3.40 1050 \n", + "2 5.68 1.03 3.17 1185 \n", + "3 7.80 0.86 3.45 1480 \n", + "4 4.32 1.04 2.93 735 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# viewing the first few rows\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "7e83e056", + "metadata": { + "papermill": { + "duration": 0.015084, + "end_time": "2022-08-22T10:49:18.429115", + "exception": false, + "start_time": "2022-08-22T10:49:18.414031", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Preprocessing the Dataset" + ] + }, + { + "cell_type": "markdown", + "id": "cc7d0234", + "metadata": { + "papermill": { + "duration": 0.015516, + "end_time": "2022-08-22T10:49:18.460761", + "exception": false, + "start_time": "2022-08-22T10:49:18.445245", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "To begin, we check the dimensions of the dataset using the shape attribute of a pandas dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e70c6dad", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:18.495436Z", + "iopub.status.busy": "2022-08-22T10:49:18.495000Z", + "iopub.status.idle": "2022-08-22T10:49:18.501385Z", + "shell.execute_reply": "2022-08-22T10:49:18.500394Z" + }, + "papermill": { + "duration": 0.027062, + "end_time": "2022-08-22T10:49:18.503656", + "exception": false, + "start_time": "2022-08-22T10:49:18.476594", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(178, 14)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking the dimensions of the dataframe\n", + "df.shape" + ] + }, + { + "cell_type": "markdown", + "id": "4bde2556", + "metadata": { + "papermill": { + "duration": 0.015605, + "end_time": "2022-08-22T10:49:18.535739", + "exception": false, + "start_time": "2022-08-22T10:49:18.520134", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, there are 178 rows and 14 columns." + ] + }, + { + "cell_type": "markdown", + "id": "9f728db1", + "metadata": { + "papermill": { + "duration": 0.015631, + "end_time": "2022-08-22T10:49:18.567162", + "exception": false, + "start_time": "2022-08-22T10:49:18.551531", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "After that, we check for missing values. To do that, we use the isnull() and sum() functions from the Pandas library." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1b863ec6", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:18.600467Z", + "iopub.status.busy": "2022-08-22T10:49:18.599686Z", + "iopub.status.idle": "2022-08-22T10:49:18.608228Z", + "shell.execute_reply": "2022-08-22T10:49:18.607141Z" + }, + "papermill": { + "duration": 0.027633, + "end_time": "2022-08-22T10:49:18.610327", + "exception": false, + "start_time": "2022-08-22T10:49:18.582694", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Class 0\n", + "Alcohol 0\n", + "Malic acid 0\n", + "Ash 0\n", + "Alcalinity of ash 0\n", + "Magnesium 0\n", + "Total phenols 0\n", + "Flavanoids 0\n", + "Nonflavanoid phenols 0\n", + "Proanthocyanins 0\n", + "Color intensity 0\n", + "Hue 0\n", + "OD280/OD315 of diluted wines 0\n", + "Proline 0\n", + "dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking for missing values\n", + "df.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "id": "205716f2", + "metadata": { + "papermill": { + "duration": 0.015259, + "end_time": "2022-08-22T10:49:18.641132", + "exception": false, + "start_time": "2022-08-22T10:49:18.625873", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, there are no missing values." + ] + }, + { + "cell_type": "markdown", + "id": "3466f6f3", + "metadata": { + "papermill": { + "duration": 0.015193, + "end_time": "2022-08-22T10:49:18.672019", + "exception": false, + "start_time": "2022-08-22T10:49:18.656826", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Next, we check if we need to encode categorical features. To do that, we use the dtypes attribute of a Pandas dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d4216619", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:18.705538Z", + "iopub.status.busy": "2022-08-22T10:49:18.704801Z", + "iopub.status.idle": "2022-08-22T10:49:18.712081Z", + "shell.execute_reply": "2022-08-22T10:49:18.711224Z" + }, + "papermill": { + "duration": 0.026455, + "end_time": "2022-08-22T10:49:18.714124", + "exception": false, + "start_time": "2022-08-22T10:49:18.687669", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Class int64\n", + "Alcohol float64\n", + "Malic acid float64\n", + "Ash float64\n", + "Alcalinity of ash float64\n", + "Magnesium int64\n", + "Total phenols float64\n", + "Flavanoids float64\n", + "Nonflavanoid phenols float64\n", + "Proanthocyanins float64\n", + "Color intensity float64\n", + "Hue float64\n", + "OD280/OD315 of diluted wines float64\n", + "Proline int64\n", + "dtype: object" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking for encoding categorical features\n", + "df.dtypes" + ] + }, + { + "cell_type": "markdown", + "id": "fc893289", + "metadata": { + "papermill": { + "duration": 0.015457, + "end_time": "2022-08-22T10:49:18.745759", + "exception": false, + "start_time": "2022-08-22T10:49:18.730302", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, there are no categorical values. Hence, we donot need to encode any features." + ] + }, + { + "cell_type": "markdown", + "id": "e8482c27", + "metadata": { + "papermill": { + "duration": 0.015277, + "end_time": "2022-08-22T10:49:18.777016", + "exception": false, + "start_time": "2022-08-22T10:49:18.761739", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Next, we check for outliers. To do that, we make a boxplot of all the attributes in the dataset using the boxplot() function from the Pandas library and aesthetic functions from the matplotlib library." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "afd58997", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:18.810489Z", + "iopub.status.busy": "2022-08-22T10:49:18.810049Z", + "iopub.status.idle": "2022-08-22T10:49:19.208989Z", + "shell.execute_reply": "2022-08-22T10:49:19.207897Z" + }, + "papermill": { + "duration": 0.418777, + "end_time": "2022-08-22T10:49:19.211347", + "exception": false, + "start_time": "2022-08-22T10:49:18.792570", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Values')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# checking for outliers\n", + "plt.figure(figsize=(15,8))\n", + "df.boxplot()\n", + "plt.title('Boxplot of the Dataset')\n", + "plt.xlabel('Attributes')\n", + "plt.ylabel('Values')" + ] + }, + { + "cell_type": "markdown", + "id": "773cb0d1", + "metadata": { + "papermill": { + "duration": 0.015826, + "end_time": "2022-08-22T10:49:19.243624", + "exception": false, + "start_time": "2022-08-22T10:49:19.227798", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, there is a neglible amount of outliers. " + ] + }, + { + "cell_type": "markdown", + "id": "ce5f80c5", + "metadata": { + "papermill": { + "duration": 0.016128, + "end_time": "2022-08-22T10:49:19.276057", + "exception": false, + "start_time": "2022-08-22T10:49:19.259929", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Next, we use the kdeplot() function from the Seaborn library to plot the distributions of the individual columns. We also use matplotlib functions to improve the aesthetics of the plots." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bdc772a1", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:19.310670Z", + "iopub.status.busy": "2022-08-22T10:49:19.309926Z", + "iopub.status.idle": "2022-08-22T10:49:19.555167Z", + "shell.execute_reply": "2022-08-22T10:49:19.554218Z" + }, + "papermill": { + "duration": 0.265421, + "end_time": "2022-08-22T10:49:19.557624", + "exception": false, + "start_time": "2022-08-22T10:49:19.292203", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Values of Alcohol Content')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the distribution of Alcohol content\n", + "plt.figure()\n", + "sns.kdeplot(df['Alcohol'])\n", + "plt.title('Distribution of variable - Alcohol')\n", + "plt.xlabel('Values of Alcohol Content')" + ] + }, + { + "cell_type": "markdown", + "id": "66e32636", + "metadata": { + "papermill": { + "duration": 0.016477, + "end_time": "2022-08-22T10:49:19.591062", + "exception": false, + "start_time": "2022-08-22T10:49:19.574585", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, the distribution of alcohol content in the wine has two significant peaks." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cc61f738", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:19.628535Z", + "iopub.status.busy": "2022-08-22T10:49:19.627815Z", + "iopub.status.idle": "2022-08-22T10:49:19.850946Z", + "shell.execute_reply": "2022-08-22T10:49:19.850060Z" + }, + "papermill": { + "duration": 0.24513, + "end_time": "2022-08-22T10:49:19.853126", + "exception": false, + "start_time": "2022-08-22T10:49:19.607996", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Values of Malic Acid Content')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the distribution of Malic Acid content\n", + "plt.figure()\n", + "sns.kdeplot(df['Malic acid'])\n", + "plt.title('Distribution of variable - Malic Acid')\n", + "plt.xlabel('Values of Malic Acid Content')" + ] + }, + { + "cell_type": "markdown", + "id": "2e98d303", + "metadata": { + "papermill": { + "duration": 0.017767, + "end_time": "2022-08-22T10:49:19.888473", + "exception": false, + "start_time": "2022-08-22T10:49:19.870706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, the distribution of malic acid content is right skewed." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b0ad2b96", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:19.926251Z", + "iopub.status.busy": "2022-08-22T10:49:19.925579Z", + "iopub.status.idle": "2022-08-22T10:49:20.147421Z", + "shell.execute_reply": "2022-08-22T10:49:20.146144Z" + }, + "papermill": { + "duration": 0.243104, + "end_time": "2022-08-22T10:49:20.149691", + "exception": false, + "start_time": "2022-08-22T10:49:19.906587", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Values of Ash Content')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the distribution of Ash content\n", + "plt.figure()\n", + "sns.kdeplot(df['Ash'])\n", + "plt.title('Distribution of variable - Ash')\n", + "plt.xlabel('Values of Ash Content')" + ] + }, + { + "cell_type": "markdown", + "id": "960c722c", + "metadata": { + "papermill": { + "duration": 0.017718, + "end_time": "2022-08-22T10:49:20.185694", + "exception": false, + "start_time": "2022-08-22T10:49:20.167976", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, the distribution of ash content is slightly left skewed." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7fd7a5a2", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:20.223243Z", + "iopub.status.busy": "2022-08-22T10:49:20.222816Z", + "iopub.status.idle": "2022-08-22T10:49:20.438961Z", + "shell.execute_reply": "2022-08-22T10:49:20.438035Z" + }, + "papermill": { + "duration": 0.237601, + "end_time": "2022-08-22T10:49:20.441289", + "exception": false, + "start_time": "2022-08-22T10:49:20.203688", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Values of Alcalinity of Ash')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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y27dvTGdYgjA9ypMrdlFR08B3Lx3r2VgN108dTFx0JM9+uNuT7RvTUZYgTI9xpKaep97fxdxx/U+7M77TkdQ7mqunZPLahmKO1NR7FocxJ2MJwvQYT7y3k2P1jXxzzmivQ+G2s4dS19jMi2v2eh2KMW2yBGF6hLJjdTy9sogrJg4M+E1xHTF2QB+mD0vhLx/toanZOvEzwckShOkR/vDeTmobmvjG7FFeh/Kp284eyp7yGt7dVuJ1KMb4ZQnChL2So7X8+cMirp6cyciMBK/D+dQl4/qTlhDLC3lWzWSCkyUIE/b+tGIX9Y3N3B1EZw8A0ZERXD15IMsKSqiotsZqE3wsQZiwdqSmnr98tJsrJw0kKy3e63BO8G9nDKKhSfnHxv1eh2LMCSxBmLD2fx8UUVPfxJ0zA98h36nIGdiHMf0TecU68DNByBKECVvH6hp5emURc3L6BcWVS2259oxBbNh7hB2lx7wOxZjPsARhwtZfP9pN5fEGvjYrOM8eWsybPJAIgb+ts8GETHAJaIIQkbkislVECkXkPj/zzxeRdSLSKCLX+cybLyLb3cf8QMZpwk9tQxN/XLGLc0emMXlwstfhtCujTy/OG5XOq+uKabZ7IkwQCViCEJFI4HHgUiAHuElEcnyK7QFuB57zWTYF+BFwJjAd+JGI9A1UrCb8vLJuH4eP1XHnzBFeh9Ih104dxP7KWj7aVeZ1KMZ8KpBnENOBQlXdqar1wAJgXusCqlqkqhsB39FTLgHeVtVyVa0A3gbmBjBWE0aam5Wn3t/F+Mw+nD0i1etwOuTinH4kxEbx2nprrDbBI5AJIhNofQfQPndaly0rIneIyBoRWVNaWnrKgZrw8u62UnaUVvPlc4d71mNrZ/WKjuSisRn8c8shGmy0ORMkQrqRWlWfUNVcVc1NT+/+vv1NcHry/V306xPLZRMGeB1Kp1w6YQBHahr4cIdVM5ngEMgEUQwMbvV6kDst0MuaHiz/QBXvFx5m/owsYqJC6/fPBaPTiY+JZNGmA16HYgwQ2ASRB4wSkWEiEgPcCCzs4LKLgYtFpK/bOH2xO82Ydj31/i7ioiO5efoQr0PptF7Rkcwe24/Fmw9aNZMJCgFLEKraCNyF88WeD7yoqptF5AERuQpARKaJyD7geuAPIrLZXbYc+DFOkskDHnCnGdOmkqO1vL5hP9dNHURy7xivwzkll00YQEVNAx/ttGom472oQK5cVRcBi3ym/bDV8zyc6iN/yz4FPBXI+Ex4WbB6L/VNzXzhnCyvQzllM7NbqpkOct4oa1cz3gqtSlpj2tDUrCxYvYdzR6YxPD14uvTurF7RkVzoVjM1WjWT8ZglCBMW3t1Wwv7KWm4+M/TaHnxdNr4/5dX1rNpltarGW5YgTFh4btUe0hJimZPTz+tQTtvM7Ax629VMJghYgjAhb/+R4ywrKOGGaYOIjgz9j3RcTCTnj0pnSf4hVK1vJuOd0P9vMj3egry9KHDjtNCvXmoxJ6cfh6rq2FRc6XUopgezBGFCWmNTMy/k7eH8UekMTuntdThdZtaYDCIElmw55HUopgezBGFC2vKtpRyqqguLxunWUuJjyB2awtv5JV6HYnowSxAmpL2ydh9pCTHMHpPhdShd7qKcDPIPVLGvosbrUEwPZQnChKzK4w0sKyjhykkDiQqDxmlfc3L6A1bNZLwTfv9Vpsd4c9MB6puauWZKR3uRDy3D0uIZkR7PEqtmMh6xBGFC1qvrixmeHs+EzCSvQwmYi3L68dHOMqpqG7wOxfRAliBMSCo+cpxVu8q5enJmyAwKdCouzulHY7Py7lYbEMt0P0sQJiQt3LAfgKsnh2f1UovJg/uSGh/DknxrhzDdzxKECTmqyqvr9zF1aF+GpIbPvQ/+REYIF2Sn8+62Upqa7a5q070sQZiQk3/gKNsOHePqyQO9DqVbzMrO4EhNAxv2HvE6FNPDWIIwIefvG/cTFSFcPrFnJIjzR6UTIfDuVruayXQvSxAmpKgqiz85yFnDU0mJD81R4zorqXc0Zwzpy3JrqDbdzBKECSmFJcfYebiaS8aFfrfenTFrTAabiispOVrrdSimB7EEYULK4s0HgX/dZdxTzMx2hh99b9thjyMxPYklCBNSFm8+xOTByfRP6uV1KN0qZ0AfMhJjWW7tEKYbWYIwIaP4yHE2FVdyybiedfYAICLMzE5nxbZSG6vadBtLECZk/NOtXupp7Q8tZmVnUFXbyHq73NV0E0sQJmQs3nyQURkJDE9P8DoUT5wzKo2oCGF5gVUzme5hCcKEhPLqelbvKu+R1Ust+vSKZupQu9zVdB9LECYkLMk/RLPSoxMEOJe75h+o4mClXe5qAi+gCUJE5orIVhEpFJH7/MyPFZEX3PmrRCTLnR4tIs+IyCYRyReR7wYyThP8lheUMCCpF+Mz+3gdiqdaLnd9d5tVM5nAC1iCEJFI4HHgUiAHuElEcnyKfQmoUNWRwGPAw+7064FYVZ0ATAW+0pI8TM/T0NTM+9sPMzM7Pay79u6I7H6JDEjqxfICq2YygRfIM4jpQKGq7lTVemABMM+nzDzgGff5y8Bscb4BFIgXkSggDqgHqgIYqwli6/cc4WhdIxeMTvc6FM85l7tm8H7hYeob7XJXE1iBTBCZwN5Wr/e50/yWUdVGoBJIxUkW1cABYA/wM1UtD2CsJoi9u62EyAhhxsg0r0MJCjOz0zlW18ia3fYvYQIrWBuppwNNwEBgGPAtERnuW0hE7hCRNSKyprTUTrnD1bvbSpk6pC99ekV7HUpQOGdkGtGRYqPMmYALZIIoBga3ej3Inea3jFudlASUATcDb6lqg6qWAB8Aub4bUNUnVDVXVXPT0636IRyVHq3jk+IqLsi297dFQmwU07JSeMcShAmwQCaIPGCUiAwTkRjgRmChT5mFwHz3+XXAMlVVnGqlCwFEJB44CygIYKwmSK3Y7nwJWvvDZ83MTmfroaPsP3Lc61BMGAtYgnDbFO4CFgP5wIuqullEHhCRq9xiTwKpIlII3Au0XAr7OJAgIptxEs3/qerGQMVqgte720pJS4ghZ0DPvrzV16zsDAA7izABFRXIlavqImCRz7Qftnpei3NJq+9yx/xNNz1LU7Py3rZSZmVnEBHRsy9v9TUyI4HM5Dje2VrCzWcO8TocE6aCtZHaGD4prqSipsHaH/xo6d31A7vc1QSQJQgTtN7dVooInGuXt/o1MzuD6vom1hTZ5a4mMCxBmKD1fuFhxg3sQ2pCrNehBKUZI1KJiYzgnW3WDmECwxKECUq1DU1s2HOEGSPs7KEt8bFRTB+WYt1/m4CxBGGC0trdFdQ3NXP28FSvQwlqM7PT2V5yjGK73NUEgCUIE5RW7jhMZIQwbViK16EEtZbeXd+xsapNAFiCMEHpwx1lTByUREJsQK/EDnkj0hMY1DfOenc1AWEJwgSdY3WNbNxXadVLHdByuevKHYepa2zyOhwTZixBmKCTV1ROY7NaA3UHzRydQU19E3m7KrwOxYSZDiUIEfmbiFwuIpZQTMB9tKOM6Ehh6tC+XocSEmaMdC93tXYI08U6+oX/W5weVreLyEMikh3AmEwP9+HOMqYM7ktcTKTXoYSE3jFRnDk8xe6HMF2uQwlCVZeo6ueBM4AiYImIrBSRL4iIddJvukzl8QY+Ka7k7BHW/tAZM7MzKCw5xt7yGq9DMWGkw1VGIpIK3A58GVgP/BInYbwdkMhMj7R6VznNiiWITvr0clc7izBdqKNtEK8CK4DewJWqepWqvqCqXwcSAhmg6Vk+3FFGbFQEU4Ykex1KSBmeFs/glDjetXYI04U6epH5H92uuz8lIrGqWqeqJ4z0ZsypWl1UxhlD+hIbZe0PnSEizMrO4KU1+6htaKJXtB0/c/o6WsX0Ez/TPuzKQIw5WtvAlv1Vdvf0KZqZnc7xhiZW77LeXU3XaPcMQkT6A5lAnIhMAVpGbemDU91kTJdZv+cIzQrTsuzy1lNx9vA0ekVHsDT/EOfbEK2mC5ysiukSnIbpQcCjraYfBb4XoJhMD7WmqJwIgSlDLEGciriYSM4dmc6S/BLuv0oRsVH4zOlpN0Go6jPAMyJyraq+0k0xmR5qdVE54wZa/0unY05OBkvyD7HlQBXjBiZ5HY4JcSerYrpFVf8CZInIvb7zVfVRP4sZ02n1jc1s2HuEm6bb+Mqn48Ix/RDZxJItJZYgzGk7WSN1vPs3AUj08zCmS3yyv5LahmamZVkD9elIT4xlyuBk3s4/6HUoJgycrIrpD+7f/+6ecExP1TKucq41UJ+2OTn9efitAg5UHmdAUpzX4ZgQ1tEb5R4RkT4iEi0iS0WkVERuCXRwpufIK6ogK7U3GYm9vA4l5M3JyQBgyZZDHkdiQl1H74O4WFWrgCtw+mIaCfxHoIIyPUtzs7KmqNyql7rIiPQEhqXF83a+3VVtTk9HE0RLVdTlwEuqWhmgeEwPtPPwMSpqGixBdBER4aKxGXy44zBHaxu8DseEsI4miH+ISAEwFVgqIulAbeDCMj1JXpEz0I21P3SdOTn9aWhS3rXO+8xp6Gh33/cBM4BcVW0AqoF5J1tOROaKyFYRKRSR+/zMjxWRF9z5q0Qkq9W8iSLyoYhsFpFNImKV02Eqb1c5aQkxDEuLP3lh0yFTh/YlLSGGNzfZ1Uzm1HXmjqQxOPdDtF7mz20VFpFI4HFgDrAPyBORhaq6pVWxLwEVqjpSRG4EHgZucLfxF+BWVf3Y7WrczpXDVN7ucnKHptidv10oMkKYO74/r6wtpqa+kd4xdvOh6byOXsX0LPAz4Fxgmvs4WS+u04FCVd2pqvXAAk4865gHPOM+fxmYLc63xMXARlX9GEBVy1TVRmQPQwcra9lbftyqlwLgsgkDON7QxPICq2Yyp6ajPytygRxV1U6sOxPY2+r1PuDMtsqoaqOIVAKpwGhARWQxkA4sUNVHfDcgIncAdwAMGWJ34IaiPPf+h+nWg2uXO3NYKmkJMSzadIDLJw7wOhwTgjraSP0J0D+QgfiIwjlb+bz79xoRme1bSFWfUNVcVc1NT7feK0PRmqJyesdEkjOgj9ehhJ3ICOHS8QNYWnCImvpGr8MxIaijCSIN2CIii0VkYcvjJMsUA4NbvR7kTvNbxm13SALKcM423lPVw6paAyzCGd7UhJnVRRVMGZJMVGSHR781nXDZhAHUNjSzrMDuiTCd19EqpvtPYd15wCgRGYaTCG4EbvYpsxCYjzP40HXAMlVtqVr6joj0BuqBC4DHTiEGE8SqahsoOFjFN2aP8jqUsDV9WAppCbEs2nSAKyYO9DocE2I6lCBU9V0RGQqMUtUl7hd3u2Maum0KdwGL3bJPqepmEXkAWKOqC4EngWdFpBAox0kiqGqFiDyKk2QUWKSqb5ziPpogtW53BarYDXIB5FQz9eeltXuprmsk3rpSN53QoU+LiPw/nMbgFGAETuPy74ET2gVac8exXuQz7YetntcC17ex7F9wLnU1YSqvqJzICGHKkGSvQwlrl08cwLMf7WZZQQlXTrKzCNNxHa34/RpwDlAFoKrbgYxABWV6hryiCsYP7GPX6AfYtKwU+vWJ5bX1vk2AxrSvowmizr2XAfi0Qbkzl7wa8xl1jU18vPcIuVa9FHCREcI1UwbxzrZSSo/WeR2OCSEdTRDvisj3gDgRmQO8BPw9cGGZcPdJcSV1jTZAUHe5bmomTc3K6xvsLMJ0XEcTxH1AKbAJ+ApOu8L3AxWUCX/WQV/3GpmRyKTByby8dh+du9/V9GQd7ayvGXgNuFNVr1PVP3byrmpjPiNvVznD0+JJS4j1OpQe47qpgyg4eJTN+6u8DsWEiHYThDjuF5HDwFZgqzua3A/bW86Y9jQ3K2t2V1j1Uje7cuIAYiIjeHntPq9DMSHiZGcQ38S5emmaqqaoagpOf0rniMg3Ax6dCUvbSo5SebyBadb/UrdK7h3DnJx+LPx4P/WNzV6HY0LAyRLErcBNqrqrZYKq7gRuAW4LZGAmfOXtcjvoszOIbnft1EzKq+tZvtW63jAnd7IEEa2qh30nqmopEB2YkEy4yyuqoF+fWAanxHkdSo9z/qh0MhJjWbB6j9ehmBBwsgRRf4rzjPFLVckrKic3ywYI8kJUZAQ3nzmEd7aVUnS42utwTJA7WYKYJCJVfh5HgQndEaAJL/sqjnOgstaqlzx08/QhRIrwl492ex2KCXLtJghVjVTVPn4eiapqVUym01oGCLIrmLyT0acXl04YwItr9to4EaZd1gm/6VZ5ReUk9ooiu3+i16H0aPPPHkpVbSOvb9jvdSgmiFmCMN1q9a5ycof2JTLC2h+8NHVoX3IG9OGZlUV2Z7VpkyUI023KjtWxo7Ta7n8IAiLC/BlDKTh49NNuT4zxZQnCdJuWLyJroA4OV03KJCkumiff3+l1KCZIWYIw3SavqJyYqAgmDEryOhQDxMVEctvZQ/nnlkMUlhz1OhwThCxBmG6TV1TO5MHJxEa1O1qt6Ua3z8giNiqC371jZxHmRJYgTLeormtk8/4qq14KMqkJsdw4bQivbyim+Mhxr8MxQcYShOkW6/ZU0NSs1kAdhP7f+cMB+ON7dhZhPssShOkWebvKiRA4Y0iy16EYH5nJcVwzJZMFeXsoO2ZDkpp/sQRhusXqonJyBvYhsZfdgB+MvjpzBHWNzTz1wa6TFzY9hiUIE3D1jc2s33PEutcIYiPSE7hswgCe/qDIziLMpyxBmIDbVFxJXWOzNVAHuW9eNIrjDU38wdoijMsShAm4VbvKAMi1BBHURmYkcvWUTJ5ZWcShqlqvwzFBwBKECbgPd5SR3S+R9MRYr0MxJ3HP7NE0NSuPLy/0OhQTBAKaIERkrohsFZFCEbnPz/xYEXnBnb9KRLJ85g8RkWMi8u1AxmkCp66xibyics4ekep1KKYDhqT25nPTBvP86j3sLa/xOhzjsYAlCBGJBB4HLgVygJtEJMen2JeAClUdCTwGPOwz/1HgzUDFaAJv/Z4j1DY0c87INK9DMR309QtHIiL8aul2r0MxHgvkGcR0oFBVd6pqPbAAmOdTZh7wjPv8ZWC2uONQisjVwC5gcwBjNAG2svAwEQLT7Qa5kDEgKY7bzhrKK+v2sfWg9dHUkwUyQWQCe1u93udO81tGVRuBSiBVRBKA/wT+u70NiMgdIrJGRNaUlpZ2WeCm66zcUcaEzCSS4uz+h1DytVkjiY+N4uG3CrwOxXgoWBup7wceU9Vj7RVS1SdUNVdVc9PT07snMtNh1XWNbNh7hBlWvRRy+sbHcOfMkSwrKOGjnWVeh2M8EsgEUQwMbvV6kDvNbxkRiQKSgDLgTOARESkC7gG+JyJ3BTBWEwCri8ppbFZmWAN1SPrCOVkMSOrF/7xZYKPO9VCBTBB5wCgRGSYiMcCNwEKfMguB+e7z64Bl6jhPVbNUNQv4BfBTVf1NAGM1AfDhjjJiIiPIHWrtD6GoV3Qk984Zzcd7j7Bo00GvwzEeCFiCcNsU7gIWA/nAi6q6WUQeEJGr3GJP4rQ5FAL3AidcCmtC1weFh5kyJJm4GBv/IVT92xmDGNM/kUcWF1Df2Ox1OKabBbQNQlUXqepoVR2hqg+6036oqgvd57Wqer2qjlTV6ap6wj3+qnq/qv4skHGarldRXc+WA1XMGGHtD6EsMkL4z7lj2F1Ww/Or93gdjulmwdpIbULcRzvLUIVzRlr7Q6ibmZ3O2cNT+dXS7RytbfA6HNONLEGYgHhveykJsVFMHJTsdSjmNIkI371sDGXV9TaoUA9jCcJ0OVVleUEp541KIybKPmLhYOKgZK6YOIA/rthFiXXk12PYf6/pclsOVHGwqpZZ2Rleh2K60H9ckk1jczOPLbEuOHoKSxCmy72z1bmrfeYYu3kxnAxNjefzZw7lxTV7KSxp9x5WEyYsQZgut6yghAmZSWQk9vI6FNPFvn7hSOKiI3nEuuDoESxBmC5VUV3P+j0VzBpj1UvhKDUhlq9eMJx/bjnEmqJyr8MxAWYJwnSp97aX0qwwK9uql8LVl84dTr8+sfx0Ub51wRHmLEGYLrWsoITU+Bgm2eWtYSsuJpJvXjSadXuOsHjzIa/DMQFkCcJ0maZm5d1tpVyQnU5EhHgdjgmg66YOYmRGAo+8VUBDk3XBEa4sQZgus2FvBUdqGuzy1h4gKjKC++aOYefhal7I23vyBUxIsgRhusyS/BIiI4TzR1n7Q08we2wG07NS+MWS7VTXNXodjgkASxCmS6gqb246wIwRqST1ttHjegIR4b7LxnD4WB1/WrHL63BMAFiCMF2i4OBRispquHT8AK9DMd3ojCF9uXR8f/7w3g5Kj9Z5HY7pYpYgTJd4c9MBIgQuHtfP61BMN/uPS7Kpb2zmV0utC45wYwnCdIlFnxzkzGGppCXEeh2K6WbD0xO4afoQnl+9h12Hq70Ox3QhSxDmtOUfqKKw5BiXTejvdSjGI3fPHkVsVAT/u9i64AgnliDMaXttfTFREcLlEwd6HYrxSHpiLHecP4JFmw6ybk+F1+GYLmIJwpyWpmbl9Q37mZmdTkp8jNfhGA99+bxhpCXE8tCiAuuCI0xYgjCnZdWuMg5W1TJvcqbXoRiPxcdGcc9Fo1hdVM7S/BKvwzFdwBKEOS2vrC0mITaKi8ba1UsGbpg2mOFp8Tz0VgGN1gVHyLMEYU5Z5fEG3ti0n6smDyQuJtLrcEwQiI6M4D8vHUNhyTEWWBccIc8ShDllr28oprahmZunD/E6FBNELs7px5nDUnjs7W1U1TZ4HY45DZYgzClRVZ5btYcJmUmMz0zyOhwTRESEH1yRQ3lNPY8vL/Q6HHMaLEGYU5JXVEHBwaPcZGcPxo/xmUlce8Yg/u/9IvaU1XgdjjlFliDMKXny/Z0k947mmil29ZLx7z8uySYyQnjorXyvQzGnKKAJQkTmishWESkUkfv8zI8VkRfc+atEJMudPkdE1orIJvfvhYGM03TO7rJq/rnlELecOdQap02b+vXpxVcvcG6ey7Pxq0NSwBKEiEQCjwOXAjnATSKS41PsS0CFqo4EHgMedqcfBq5U1QnAfODZQMVpOu/J93cRFSHcdvZQr0MxQe6O84fTv08vfvyPLTQ3281zoSaQZxDTgUJV3amq9cACYJ5PmXnAM+7zl4HZIiKqul5V97vTNwNxImK9wAWBA5XHWbB6L9eeMYiMPr28DscEubiYSL4zN5uN+yp5bUOx1+GYTgpkgsgEWl8Ivc+d5reMqjYClUCqT5lrgXWqekJn8yJyh4isEZE1paWlXRa4adtvl++gWZWvzRrpdSgmRFw9OZNJg5P56aICKmvsstdQEtSN1CIyDqfa6Sv+5qvqE6qaq6q56ek2zGWg7S2v4YW8vVyfO5jBKb29DseEiIgI4cGrx1NeXcfD1ttrSAlkgigGBrd6Pcid5reMiEQBSUCZ+3oQ8Cpwm6ruCGCcpoMeerOAyAjh7tl29mA6Z3xmEl84ZxjPrdrD2t3W22uoCGSCyANGicgwEYkBbgQW+pRZiNMIDXAdsExVVUSSgTeA+1T1gwDGaDroo51lvLHpAP8+cwQDkuK8DseEoHvnjGZgUi++97dNNFg/TSEhYAnCbVO4C1gM5AMvqupmEXlARK5yiz0JpIpIIXAv0HIp7F3ASOCHIrLBfWQEKlbTvtqGJn7w2idkJsdxx/nDvQ7HhKj42Cjuv2ocWw8d5Y8rdnodjumAqECuXFUXAYt8pv2w1fNa4Ho/y/0E+EkgYzMd94sl29lecoxnvjidXtF234M5dReP68+l4/vz2NvbmDk6g5yBfbwOybQjqBupjfdW7Szjifd2cEPuYC4YbRcCmNP34DUT6Ns7hm8sWE9tQ5PX4Zh2WIIwbTpUVcvXnltPVmo8379irNfhmDCREh/D/14/ie0lx3joTbuqKZhZgjB+Hatr5I4/r6GmvpHf3zqVxF7RXodkwsgFo9O5fUYWT68s4p2tNvpcsLIEYU5Q29DEV55dwyf7q/jljVMY3S/R65BMGLrv0jFk90vkmy9ssB5fg5QlCPMZlTUN3PrkKj4oLOORaycyJ8eGEjWB0Ss6kt/fOpVmhS89k8dRG1wo6FiCMJ/avL+Sa377AR/vreQ3N0/h2qmDvA7JhLlhafH87vNnsOtwNd9YsIEm69AvqFiCMNQ1NvHrpdu55rcrqa5v5C9fPpMrJg70OizTQ8wYmcb9V41jWUEJD76Rj6oliWAR0PsgTHCra2zitfXF/GZ5IXvLj3PZhP78eN54UhOs41zTvW45ayg7So/x1Ae7SOgVxb1zRnsdksESRI/T0NTM2t0VLNlyiFfXF1NWXc/EQUn89JoJnDfK7nMw3vnB5TnU1DXxq6XbaWpu5tsXZyMiXofVo1mC6AHKq+t5Z2sJSwtKeG9bKUdrG4mOFGZlZ3Dr2UM5d2Sa/SMaz0VECD/9twlERAiPL99B2bF6fnz1eKIjrSbcK5YgwpCqsuVAFcsLSlhWUML6vUdQhbSEWOaO68/ssRmcMzLN7m0wQScyQvjpNeNJjY/hN8sL2Xm4msdvPoP0RKv29IKES4NQbm6urlmzxuswPKOqbCqu5O8f7+eNjQfYX1kLwMRBSVw4JoMLx2QwfmASERF2pmBCw2vri/nPVzaS2CuKR66byIVj7JLrQBCRtaqa62+enUGEuMrjDby0Zi/PrdrDzsPVREcK549K5545o5mZnU5Gog0LakLT1VMyGTugD3c/v54vPr2Gi8b24wdXjGVoarzXofUYdgYRovaW1/D7d3fwt3XFHG9oIndoX67PHcTccQNI6m1VRyZ81DU28dT7Rfx62XYam5Tbz8nii+cMo3+S/fjpCu2dQViCCDF7y2t4fHkhL6/dR0SEMG/SQObPyGJ8ZpLXoRkTUIeqann4zQJe3VBMpAiXTRjA/BlZnDEk2S6yOA2WIMLA0doGfr2skP/7YBeCcNP0wfz7zJH2K8r0OHvKanh6ZREvrtnLsbpGMpPjuGxCf+aO78+kQclE2VVPnWIJIoQ1Nyt/W1/MQ28WcPhYHddPHcQ354xmYLIN+2l6tqO1Dbz1yUHe/OQgK7aX0tCkJMRGceawFM4ekcqMEWmM6Z9oF2achDVSh6iP9x7hRws3s2HvESYPTubJ+blMGpzsdVjGBIXEXtFcnzuY63MHU3m8gfe2lfLhzjI+3FHG0gKnC/Hk3tFMy0rhzGEpnDU8lbED+hBpCaPDLEEEodKjdTzyVgEvrd1HemIsP79+EtdMybRfQsa0ISkumisnDeTKSU4fYgcqj7OysIxVu8pYtauct7ccAiAxNorcrL6cOTyV6cNSmJCZZDfitcOqmIJIfWMzz6ws4ldLt1Pb2MQXzxnGXReOtBvajDlNBytrP00Wq3aWsaO0GoDeMZFMy0phVnY6F47px5DU3h5H2v2sDSLIqSqLNx/ioTfzKSqrYWZ2Oj+8Iofh6Qleh2ZMWCo9WkdekZMsVmw/zM7DTsIYkR7PrGznxtLcrBRiosL/7MISRBD7pLiSn7yxhY92ljMqI4H/unwsM7MzvA7LmB6l6HA1ywpKWL61hFU7y6lvaiYxNorzs9O5aGwGs7IzSO4d43WYAWEJIghtP3SUXy8r5O8b95McF829c0Zz0/QhdomeMR6rrmvkg8LDLCsoYUl+CYeP1REZIUwd2pc5Y/sxe2xGWJ3dW4IIIh/vPcIT7+1k0ScHiIuO5Nazh3LnzJEkxVk7gzHBprlZ2VhcyZIth1iSf4iCg0cBGJ4Wz0U5/Zg9JoOpQ/uG9A87SxAeq6lv5B8fH+DZj3azqbiShNgobp+RxRfPHUZKfHiethoTjvZV1LA0v4Ql+Yf4aGcZDU1Kcu/oT9stzh6RSlqIDbhlCcIDlTUNvLOthEWbDvDutlJqG5oZlZHArWcP5ZopmXZlkjEh7mhtAyu2H2bJlkMs31pCRU0DACMzEjhzWApnDk9l6tC+DEzqFdRdgXiWIERkLvBLIBL4k6o+5DM/FvgzMBUoA25Q1SJ33neBLwFNwN2quri9bXmZIBqbmtl1uJqCg0dZt6eCVTvLyT9YhSpkJMYyd3x/rpg4kGlZfYP6g2KMOTWNTc18vK+SVbvKWL2rnDVFFRyrawScm/XGDexDzoA+5Azsw7C0BIak9KZv7+ig+D7wJEGISCSwDZgD7APygJtUdUurMncCE1X1qyJyI3CNqt4gIjnA88B0YCCwBBitqk1tbe90EoSq0tCkNDQ1U9/YTENTM3Xu3/qmZmobmqk83sCRmnqO1DRwpKaBQ0drKa44zv4jx9ldVkN9UzMAsVERnDGkL2cNT+XcUalMGdzXbnAzpodpbGpmy4EqPt5XyZb9lWzeX0XBwaPUNzZ/WiY+JpLBKb3JTI4jNSGGlPhYUuNjSImPIaFXFL1jIukdE0mv6Eh6xzivYyIjiIwUoiKEyAghUpy/p5NovOpqYzpQqKo73SAWAPOALa3KzAPud5+/DPxGnD2dByxQ1Tpgl4gUuuv7sKuDLD1ax7QHl3R6ub69o8nsG8fw9HguHJNBdv9ExvTvw8iMhB5x7bQxpm1RkRFMHJTMxEHJn05rqWnYXVbDnnLnsa+ihuIjtXyyv5Ly6noamk7tB/sZQ5L5253ndFH0/xLIBJEJ7G31eh9wZltlVLVRRCqBVHf6Rz7LZvpuQETuAO5wXx4Tka1dE/rJ7QY2BGbVacDhwKw6aNg+ho+esJ9Bv4+7AfnaKS8+tK0ZId0Xk6o+ATzhdRxdSUTWtHW6Fy5sH8NHT9jPnrCPbQlkXUgxMLjV60HuNL9lRCQKSMJprO7IssYYYwIokAkiDxglIsNEJAa4EVjoU2YhMN99fh2wTJ1W84XAjSISKyLDgFHA6gDGaowxxkfAqpjcNoW7gMU4l7k+paqbReQBYI2qLgSeBJ51G6HLcZIIbrkXcRq0G4GvtXcFU5gJqyqzNtg+ho+esJ89YR/9Cpsb5YwxxnQtux7TGGOMX5YgjDHG+GUJwiMi8pSIlIjIJ62mpYjI2yKy3f3b18sYu0Ib+3m/iBSLyAb3cZmXMZ4uERksIstFZIuIbBaRb7jTw+b9bGcfw+297CUiq0XkY3c//9udPkxEVolIoYi84F54E/YsQXjnaWCuz7T7gKWqOgpY6r4OdU9z4n4CPKaqk93Hom6Oqas1At9S1RzgLOBrbncx4fR+trWPEF7vZR1woapOAiYDc0XkLOBhnP0cCVTg9BMX9ixBeERV38O5cqu1ecAz7vNngKu7M6ZAaGM/w4qqHlDVde7zo0A+zp3/YfN+trOPYUUdx9yX0e5DgQtxugOCEH8vO8MSRHDpp6oH3OcHgX5eBhNgd4nIRrcKKmSrXnyJSBYwBVhFmL6fPvsIYfZeikikiGwASoC3gR3AEVVtdIv47fonHFmCCFLuDYPheg3y74AROKfwB4CfexpNFxGRBOAV4B5VrWo9L1zeTz/7GHbvpao2qepknB4cpgNjvI3IO5YggsshERkA4P4t8TiegFDVQ+4/YTPwR5x/wpAmItE4X5x/VdW/uZPD6v30t4/h+F62UNUjwHLgbCDZ7Q4IelDXP5YggkvrrkfmA697GEvAtHxpuq4BPmmrbChwu6h/EshX1UdbzQqb97OtfQzD9zJdRJLd53E449nk4ySK69xiIf1edobdSe0REXkemInTlfAh4EfAa8CLwBCcHnw/p6oh3cDbxn7OxKmSUKAI+EqruvqQIyLnAiuATUDLiDDfw6mjD4v3s519vInwei8n4jRCR+L8gH5RVR8QkeHAAiAFWA/c4o5XE9YsQRhjjPHLqpiMMcb4ZQnCGGOMX5YgjDHG+GUJwhhjjF+WIIwxxvhlCcIEnNsL6CU+0+4Rkd+1s8w7ItLtA8WLyN0iki8if21j/i/c3ksjWk27XUR+c4rbKxKRNPf5yg6U/1NLJ3ki8r1T2WYb6+30frdRbqaI/KOr4jLesgRhusPzuMPJtnKjOz3Y3AnMUdXP+85wvxyvAfYCF3T1hlV1RgfKfFlVt7gvuyxB4OF+m+BlCcJ0h5eBy1v60Hc7exsIrBCR34nImtZ97/sSkWOtnl8nIk+7z9NF5BURyXMf57jTL2g1PsF6EUn0s857ReQT93GPO+33wHDgTRH5pp9QZgKbcfofuqmNWPuJyKvueAIfi8gMd/prIrLW3c872ttP91f4OyLysogUiMhf3TuZPz2zEpGHgDh3H/8qIg+07Idb7kFxx2wI1H63c5wT/MVuQpCq2sMeAX8A/wDmuc/vA37mPk9x/0YC7wAT3dfvALnu82Ot1nMd8LT7/DngXPf5EJxuIAD+DpzjPk8AonximYpzR3C8O38zMMWdVwSktbEPfwRuBfrg9MUT7U6/HfiN+/wFnI7sWvYpyWc/43C6o0j13V7LfuJ8IVfi9PkTAXzYaj/bOi5ZwDr3eQROD6SpAd7vE45ze7HbI/QedgZhukvraqbW1UufE5F1ON0XjANy/CzblouA37hdMy8E+ri9jX4APCoidwPJ+q9umlucC7yqqtXq9P3/N+C89jbknv1cBrymTi+mq4BL/BS9EOeXNup0YlfpTr9bRD4GPgIGA6NOsm+rVXWfOp3gbcBJAG1S1SKgTESmABcD61W1zKdYV+93W8e5U7Gb4BV18iLGdInXgcdE5Aygt6quFZFhwLeBaapa4VYd9fKzbOv+YFrPjwDOUtVan/IPicgbOF9sH4jIJapacJrxXwIkA5vcGpPewHGcM6N2ichMnGR2tqrWiMg7+N/P1lr389NEx/5X/4RzNtMfeKoD5Tuizf1W1ROOs7vMqcRugpCdQZhu4f5iXY7zxdVy9tAHqAYqRaQfcGkbix8SkbGtGktb/BP4essLEZns/h2hqptU9WEgjxP7818BXC0ivUUk3l3nipPswk3Al1U1S1WzgGHAHBHp7VNuKfDvbhyRIpIEJAEVbnIYgzNkZ1doEKcL7hav4gzvOg1Y7Kd8l+53B46zCXGWIEx3eh6Y5P5FVT/GqVoqwGlP+KCN5e7D+aW+EmdQmhZ3A7nijGa2BfiqO/0etxF2I9AAvNl6ZeoMnfk0sBqnyuRPqrq+raDdJDAXeKPVOqqB94ErfYp/A5glIpuAtThVZm8BUSKSDzyEU83UFZ4ANrZcmqqq9ThJ+EVVbfItHID9bvc4m9BnvbkaEybcM6x1wPWqut3reEzoszMIY8KAODfPFQJLLTmYrmJnEMYYY/yyMwhjjDF+WYIwxhjjlyUIY4wxflmCMMYY45clCGOMMX79fz7Zt8fK7wB4AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the distribution of Alcalinity of Ash\n", + "plt.figure()\n", + "sns.kdeplot(df['Alcalinity of ash'])\n", + "plt.title('Distribution of variable - Alcalinity of Ash')\n", + "plt.xlabel('Values of Alcalinity of Ash')" + ] + }, + { + "cell_type": "markdown", + "id": "10298c0c", + "metadata": { + "papermill": { + "duration": 0.017743, + "end_time": "2022-08-22T10:49:20.477420", + "exception": false, + "start_time": "2022-08-22T10:49:20.459677", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, the alcalinity of ash is more or less normally distributed and is neglibly skewed." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5faaf702", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:20.516091Z", + "iopub.status.busy": "2022-08-22T10:49:20.515666Z", + "iopub.status.idle": "2022-08-22T10:49:20.737306Z", + "shell.execute_reply": "2022-08-22T10:49:20.736137Z" + }, + "papermill": { + "duration": 0.244565, + "end_time": "2022-08-22T10:49:20.740038", + "exception": false, + "start_time": "2022-08-22T10:49:20.495473", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Values of Magnesium Content')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the distribution of Magnesium content\n", + "plt.figure()\n", + "sns.kdeplot(df['Magnesium'])\n", + "plt.title('Distribution of variable - Magnesium')\n", + "plt.xlabel('Values of Magnesium Content')" + ] + }, + { + "cell_type": "markdown", + "id": "26bfee56", + "metadata": { + "papermill": { + "duration": 0.018398, + "end_time": "2022-08-22T10:49:20.777175", + "exception": false, + "start_time": "2022-08-22T10:49:20.758777", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, the distribution of magnesium content is slightly right skewed." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "27240c4c", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:20.816799Z", + "iopub.status.busy": "2022-08-22T10:49:20.816327Z", + "iopub.status.idle": "2022-08-22T10:49:21.025126Z", + "shell.execute_reply": "2022-08-22T10:49:21.024201Z" + }, + "papermill": { + "duration": 0.231197, + "end_time": "2022-08-22T10:49:21.027388", + "exception": false, + "start_time": "2022-08-22T10:49:20.796191", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Values of Total Phenols Content')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the distribution of Total Phenols content\n", + "plt.figure()\n", + "sns.kdeplot(df['Total phenols'])\n", + "plt.title('Distribution of variable - Total Phenols')\n", + "plt.xlabel('Values of Total Phenols Content')" + ] + }, + { + "cell_type": "markdown", + "id": "aa12151c", + "metadata": { + "papermill": { + "duration": 0.019038, + "end_time": "2022-08-22T10:49:21.065728", + "exception": false, + "start_time": "2022-08-22T10:49:21.046690", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, the distribution of total phenols content is has two significant peaks." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1294d18e", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:21.108686Z", + "iopub.status.busy": "2022-08-22T10:49:21.107908Z", + "iopub.status.idle": "2022-08-22T10:49:21.333010Z", + "shell.execute_reply": "2022-08-22T10:49:21.332161Z" + }, + "papermill": { + "duration": 0.24908, + "end_time": "2022-08-22T10:49:21.335527", + "exception": false, + "start_time": "2022-08-22T10:49:21.086447", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Values of Flavanoids Content')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the distribution of Flavanoids content\n", + "plt.figure()\n", + "sns.kdeplot(df['Flavanoids'])\n", + "plt.title('Distribution of variable - Flavanoids')\n", + "plt.xlabel('Values of Flavanoids Content')" + ] + }, + { + "cell_type": "markdown", + "id": "88b4ee01", + "metadata": { + "papermill": { + "duration": 0.019349, + "end_time": "2022-08-22T10:49:21.374669", + "exception": false, + "start_time": "2022-08-22T10:49:21.355320", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, the distribution of flavanoids content has two significant peaks." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ab6447da", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:21.417264Z", + "iopub.status.busy": "2022-08-22T10:49:21.416850Z", + "iopub.status.idle": "2022-08-22T10:49:21.647135Z", + "shell.execute_reply": "2022-08-22T10:49:21.646293Z" + }, + "papermill": { + "duration": 0.254907, + "end_time": "2022-08-22T10:49:21.649410", + "exception": false, + "start_time": "2022-08-22T10:49:21.394503", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Values of Nonflavanoid Phenols Content')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the distribution of Nonflavanoid Phenols content\n", + "plt.figure()\n", + "sns.kdeplot(df['Nonflavanoid phenols'])\n", + "plt.title('Distribution of variable - Nonflavanoid Phenols')\n", + "plt.xlabel('Values of Nonflavanoid Phenols Content')" + ] + }, + { + "cell_type": "markdown", + "id": "6201121e", + "metadata": { + "papermill": { + "duration": 0.020911, + "end_time": "2022-08-22T10:49:21.690982", + "exception": false, + "start_time": "2022-08-22T10:49:21.670071", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, the distribution of nonflavanoid phenols content is slightly right skewed." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ebffa213", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:21.734131Z", + "iopub.status.busy": "2022-08-22T10:49:21.733347Z", + "iopub.status.idle": "2022-08-22T10:49:21.951606Z", + "shell.execute_reply": "2022-08-22T10:49:21.950443Z" + }, + "papermill": { + "duration": 0.242804, + "end_time": "2022-08-22T10:49:21.954186", + "exception": false, + "start_time": "2022-08-22T10:49:21.711382", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Values of Proanthocyanins Content')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the distribution of Proanthocyanins content\n", + "plt.figure()\n", + "sns.kdeplot(df['Proanthocyanins'])\n", + "plt.title('Distribution of variable - Proanthocyanins')\n", + "plt.xlabel('Values of Proanthocyanins Content')" + ] + }, + { + "cell_type": "markdown", + "id": "77f904c1", + "metadata": { + "papermill": { + "duration": 0.024289, + "end_time": "2022-08-22T10:49:22.000611", + "exception": false, + "start_time": "2022-08-22T10:49:21.976322", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, the distribution of proanthocyanins is slightly right skewed." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8c628c8f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:22.043234Z", + "iopub.status.busy": "2022-08-22T10:49:22.042848Z", + "iopub.status.idle": "2022-08-22T10:49:22.270783Z", + "shell.execute_reply": "2022-08-22T10:49:22.269656Z" + }, + "papermill": { + "duration": 0.252759, + "end_time": "2022-08-22T10:49:22.273909", + "exception": false, + "start_time": "2022-08-22T10:49:22.021150", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Values of Color Intensity')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the distribution of Color Intensity\n", + "plt.figure()\n", + "sns.kdeplot(df['Color intensity'])\n", + "plt.title('Distribution of variable - Color Intensity')\n", + "plt.xlabel('Values of Color Intensity')" + ] + }, + { + "cell_type": "markdown", + "id": "c2af03cb", + "metadata": { + "papermill": { + "duration": 0.020948, + "end_time": "2022-08-22T10:49:22.316539", + "exception": false, + "start_time": "2022-08-22T10:49:22.295591", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, the distribution of color intensity is right skewed." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "8d5a0ae6", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:22.361574Z", + "iopub.status.busy": "2022-08-22T10:49:22.361148Z", + "iopub.status.idle": "2022-08-22T10:49:22.591440Z", + "shell.execute_reply": "2022-08-22T10:49:22.590202Z" + }, + "papermill": { + "duration": 0.255808, + "end_time": "2022-08-22T10:49:22.594259", + "exception": false, + "start_time": "2022-08-22T10:49:22.338451", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Values of Hue')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the distribution of Hue\n", + "plt.figure()\n", + "sns.kdeplot(df['Hue'])\n", + "plt.title('Distribution of variable - Hue')\n", + "plt.xlabel('Values of Hue')" + ] + }, + { + "cell_type": "markdown", + "id": "f524ebbb", + "metadata": { + "papermill": { + "duration": 0.021313, + "end_time": "2022-08-22T10:49:22.637832", + "exception": false, + "start_time": "2022-08-22T10:49:22.616519", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, the distribution of hue is not smooth and has peaks. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "7e3d0cdc", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:22.683144Z", + "iopub.status.busy": "2022-08-22T10:49:22.682127Z", + "iopub.status.idle": "2022-08-22T10:49:22.893327Z", + "shell.execute_reply": "2022-08-22T10:49:22.892178Z" + }, + "papermill": { + "duration": 0.236391, + "end_time": "2022-08-22T10:49:22.895742", + "exception": false, + "start_time": "2022-08-22T10:49:22.659351", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Values of OD280/OD315 of diluted wines')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the distribution of Values of OD280/OD315 of diluted wine\n", + "plt.figure()\n", + "sns.kdeplot(df['OD280/OD315 of diluted wines'])\n", + "plt.title('Distribution of variable - OD280/OD315 of diluted wines')\n", + "plt.xlabel('Values of OD280/OD315 of diluted wines')" + ] + }, + { + "cell_type": "markdown", + "id": "6f247f7e", + "metadata": { + "papermill": { + "duration": 0.021971, + "end_time": "2022-08-22T10:49:22.939758", + "exception": false, + "start_time": "2022-08-22T10:49:22.917787", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, the distribution of OD280/OD315 of diluted wine has two significant peaks." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "df905ad1", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:22.986413Z", + "iopub.status.busy": "2022-08-22T10:49:22.985662Z", + "iopub.status.idle": "2022-08-22T10:49:23.200929Z", + "shell.execute_reply": "2022-08-22T10:49:23.200134Z" + }, + "papermill": { + "duration": 0.240784, + "end_time": "2022-08-22T10:49:23.203063", + "exception": false, + "start_time": "2022-08-22T10:49:22.962279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Values of Proline Content')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the distribution of Proline content\n", + "plt.figure()\n", + "sns.kdeplot(df['Proline '])\n", + "plt.title('Distribution of variable - Proline ')\n", + "plt.xlabel('Values of Proline Content')" + ] + }, + { + "cell_type": "markdown", + "id": "650abf85", + "metadata": { + "papermill": { + "duration": 0.022181, + "end_time": "2022-08-22T10:49:23.247946", + "exception": false, + "start_time": "2022-08-22T10:49:23.225765", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, the distribution of proline is slightly right skewed." + ] + }, + { + "cell_type": "markdown", + "id": "8db00123", + "metadata": { + "papermill": { + "duration": 0.022507, + "end_time": "2022-08-22T10:49:23.292845", + "exception": false, + "start_time": "2022-08-22T10:49:23.270338", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Although we do find some anomalies in the distribution, since the amount of data we have is less, we will not perform any transformations." + ] + }, + { + "cell_type": "markdown", + "id": "3dab52d9", + "metadata": { + "papermill": { + "duration": 0.022697, + "end_time": "2022-08-22T10:49:23.338157", + "exception": false, + "start_time": "2022-08-22T10:49:23.315460", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "In this notebook, we plan to build a non-parametric model which does not assume a normal distribution. Hence, we donot scale the data." + ] + }, + { + "cell_type": "markdown", + "id": "4ee95a67", + "metadata": { + "papermill": { + "duration": 0.021974, + "end_time": "2022-08-22T10:49:23.382499", + "exception": false, + "start_time": "2022-08-22T10:49:23.360525", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Model Building" + ] + }, + { + "cell_type": "markdown", + "id": "f6e8ee8d", + "metadata": { + "papermill": { + "duration": 0.02189, + "end_time": "2022-08-22T10:49:23.426944", + "exception": false, + "start_time": "2022-08-22T10:49:23.405054", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "In this notebook, we will be building a Random Forest model for classification." + ] + }, + { + "cell_type": "markdown", + "id": "87c12ab7", + "metadata": { + "papermill": { + "duration": 0.021975, + "end_time": "2022-08-22T10:49:23.470935", + "exception": false, + "start_time": "2022-08-22T10:49:23.448960", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "To begin, we split the data into independent variables (x) and dependent variable (y) and use the train_test_split() function from the sklearn library and divide the dataset into training and testing sets." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "3b468f0a", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:23.518634Z", + "iopub.status.busy": "2022-08-22T10:49:23.517270Z", + "iopub.status.idle": "2022-08-22T10:49:23.878975Z", + "shell.execute_reply": "2022-08-22T10:49:23.877676Z" + }, + "papermill": { + "duration": 0.388523, + "end_time": "2022-08-22T10:49:23.881726", + "exception": false, + "start_time": "2022-08-22T10:49:23.493203", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# splitting the data into independent and dependent variables\n", + "x = df.drop(columns=['Class'])\n", + "y = df['Class']\n", + "\n", + "# diving the dataset into training and testing sets\n", + "from sklearn.model_selection import train_test_split\n", + "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7, random_state=105)" + ] + }, + { + "cell_type": "markdown", + "id": "9e3d262a", + "metadata": { + "papermill": { + "duration": 0.022442, + "end_time": "2022-08-22T10:49:23.926697", + "exception": false, + "start_time": "2022-08-22T10:49:23.904255", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "After that, we build our model. To do that, we use the RandomForestClassifier() module from the sklearn library, and fix the random state to be 105 for consistent results." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "2652f0d4", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:23.973810Z", + "iopub.status.busy": "2022-08-22T10:49:23.973278Z", + "iopub.status.idle": "2022-08-22T10:49:24.211805Z", + "shell.execute_reply": "2022-08-22T10:49:24.210155Z" + }, + "papermill": { + "duration": 0.265919, + "end_time": "2022-08-22T10:49:24.215057", + "exception": false, + "start_time": "2022-08-22T10:49:23.949138", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# building the model\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "model = RandomForestClassifier(random_state=105)" + ] + }, + { + "cell_type": "markdown", + "id": "726334ec", + "metadata": { + "papermill": { + "duration": 0.022941, + "end_time": "2022-08-22T10:49:24.261448", + "exception": false, + "start_time": "2022-08-22T10:49:24.238507", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "To optimize the model, we create a dictionary of the parameters and a list of their values that we want to search within for optimization. After that, we build a grid search model and fit the training data to analyze and test all the combinations of the parameters on a Random Forest Classifer model with 10 cross validations." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ad900ef1", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:49:24.310850Z", + "iopub.status.busy": "2022-08-22T10:49:24.310087Z", + "iopub.status.idle": "2022-08-22T10:55:43.875217Z", + "shell.execute_reply": "2022-08-22T10:55:43.874001Z" + }, + "papermill": { + "duration": 379.615791, + "end_time": "2022-08-22T10:55:43.900796", + "exception": false, + "start_time": "2022-08-22T10:49:24.285005", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=10, estimator=RandomForestClassifier(random_state=105),\n", + " param_grid={'criterion': ['gini', 'entropy'],\n", + " 'max_depth': [3, 4, 5, 6, 7, 8, 9, 10],\n", + " 'n_estimators': [100, 500, 1000]})" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# importing the necessary libraries\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "# creating a dictionary and list of their values to optimize the model\n", + "params = {\n", + " 'n_estimators' : [100, 500, 1000],\n", + " 'criterion' : ['gini', 'entropy'],\n", + " 'max_depth' : [3, 4, 5, 6, 7, 8, 9, 10],\n", + "}\n", + "\n", + "# intiating a grid search to find the most optimum parameters\n", + "grid_search = GridSearchCV(model, params, cv=10)\n", + "\n", + "# fitting the training data\n", + "grid_search.fit(x_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "id": "f65b09fe", + "metadata": { + "papermill": { + "duration": 0.024344, + "end_time": "2022-08-22T10:55:43.949259", + "exception": false, + "start_time": "2022-08-22T10:55:43.924915", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Next, we use the best_estimator_ attribute of the grid search model to obtain the best parameters for the Random Forest Classifer. We then fit the training data on the best model and use the classification_report() funtion from the sklearn library to tabulate the model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "91c26c20", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:55:44.000416Z", + "iopub.status.busy": "2022-08-22T10:55:43.999813Z", + "iopub.status.idle": "2022-08-22T10:55:44.748995Z", + "shell.execute_reply": "2022-08-22T10:55:44.747602Z" + }, + "papermill": { + "duration": 0.777656, + "end_time": "2022-08-22T10:55:44.751848", + "exception": false, + "start_time": "2022-08-22T10:55:43.974192", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 1 1.00 0.93 0.96 14\n", + " 2 0.96 1.00 0.98 26\n", + " 3 1.00 1.00 1.00 14\n", + "\n", + " accuracy 0.98 54\n", + " macro avg 0.99 0.98 0.98 54\n", + "weighted avg 0.98 0.98 0.98 54\n", + "\n" + ] + } + ], + "source": [ + "# obtaining the best model\n", + "model = grid_search.best_estimator_\n", + "\n", + "# fitting the training data\n", + "model.fit(x_train, y_train)\n", + "\n", + "# obtaining the classification report\n", + "from sklearn.metrics import classification_report\n", + "print(classification_report(y_test, model.predict(x_test)))" + ] + }, + { + "cell_type": "markdown", + "id": "3575101c", + "metadata": { + "papermill": { + "duration": 0.02268, + "end_time": "2022-08-22T10:55:44.798069", + "exception": false, + "start_time": "2022-08-22T10:55:44.775389", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Great, now that we have built our model, let's analyze how it is working out!" + ] + }, + { + "cell_type": "markdown", + "id": "fc96d594", + "metadata": { + "papermill": { + "duration": 0.022777, + "end_time": "2022-08-22T10:55:44.843839", + "exception": false, + "start_time": "2022-08-22T10:55:44.821062", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Explainability via LIME" + ] + }, + { + "cell_type": "markdown", + "id": "242fd564", + "metadata": { + "papermill": { + "duration": 0.023191, + "end_time": "2022-08-22T10:55:44.890108", + "exception": false, + "start_time": "2022-08-22T10:55:44.866917", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Local Interpretable Model-agnostic Explanations (LIME) - the word Local means that we can analyze the predictions of individual instances. We'll see just how in the following section! To begin, we import the lime library. Since we are dealing with tabular data, we also import lime_tabular. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "81907e7a", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:55:44.939249Z", + "iopub.status.busy": "2022-08-22T10:55:44.938521Z", + "iopub.status.idle": "2022-08-22T10:55:44.955209Z", + "shell.execute_reply": "2022-08-22T10:55:44.953936Z" + }, + "papermill": { + "duration": 0.043838, + "end_time": "2022-08-22T10:55:44.957741", + "exception": false, + "start_time": "2022-08-22T10:55:44.913903", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# importing lime\n", + "import lime\n", + "from lime import lime_tabular" + ] + }, + { + "cell_type": "markdown", + "id": "7d4f4f57", + "metadata": { + "papermill": { + "duration": 0.023987, + "end_time": "2022-08-22T10:55:45.005269", + "exception": false, + "start_time": "2022-08-22T10:55:44.981282", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Next, we create an object of the LimeTabularExplainer class from the lime_tabular module. To that, we pass our training data in the form a NumPy array (essential syntactically), the feature names (using the columns attribute of a Pandas dataframe), class names, and since this is a classification problem, we set the mode to be 'classification'." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "d339477e", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:55:45.054042Z", + "iopub.status.busy": "2022-08-22T10:55:45.053619Z", + "iopub.status.idle": "2022-08-22T10:55:45.067178Z", + "shell.execute_reply": "2022-08-22T10:55:45.066225Z" + }, + "papermill": { + "duration": 0.041017, + "end_time": "2022-08-22T10:55:45.069778", + "exception": false, + "start_time": "2022-08-22T10:55:45.028761", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# creating an instance of the lime tabular explainer\n", + "lime_explainer = lime_tabular.LimeTabularExplainer(training_data=np.array(x_train), feature_names=x_train.columns, \n", + " class_names=['1', '2', '3'], mode='classification')" + ] + }, + { + "cell_type": "markdown", + "id": "107ef5af", + "metadata": { + "papermill": { + "duration": 0.023806, + "end_time": "2022-08-22T10:55:45.117584", + "exception": false, + "start_time": "2022-08-22T10:55:45.093778", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "After that, we derive the explanation of an instance using the explain_instance function on the previously instantiated lime_explainer. Below are the parameters we have passed:\n", + "\n", + "- data_row: we pass one row of the data to be explained\n", + "- predict_fn: since we want the probability or confidence with which the classifier has made the prediction, we set the value of this parameter to be predict_proba\n", + "- top_labels: since we have 3 classes, we set this parameter to be 6 (2x3), so that we get the probability of being classified as a class and not classified as that class for each class\n", + "- num_features: since we want to know the weightage of every feature in the classification, we set the parameter to be 13 (total number of features in the notebook)\n", + "\n", + "Following that we use the show_in_notebook() function to print out the explanation." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d48ffadd", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:55:45.165606Z", + "iopub.status.busy": "2022-08-22T10:55:45.165193Z", + "iopub.status.idle": "2022-08-22T10:55:51.118758Z", + "shell.execute_reply": "2022-08-22T10:55:51.117245Z" + }, + "papermill": { + "duration": 6.040367, + "end_time": "2022-08-22T10:55:51.180624", + "exception": false, + "start_time": "2022-08-22T10:55:45.140257", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n", + " \"X does not have valid feature names, but\"\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# obtaining the explanation\n", + "explanation = lime_explainer.explain_instance(data_row=x_test.iloc[1], predict_fn=model.predict_proba, top_labels=6, num_features=13)\n", + "\n", + "# printing out the explanation\n", + "explanation.show_in_notebook()" + ] + }, + { + "cell_type": "markdown", + "id": "d432d44d", + "metadata": { + "papermill": { + "duration": 0.076688, + "end_time": "2022-08-22T10:55:51.336771", + "exception": false, + "start_time": "2022-08-22T10:55:51.260083", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see:\n", + "- the model predicts class 1 with 9% confidence, class 2 with 85% confidence, and class 3 with 6% confidence.\n", + "- each of the attribute holds certain weightage in predicting the occurence of a class depending on a particular threshold. For example, the threshold for the attribute 'proline' is 513.75, above which increases the chance of it being in class 2 or 3 with weightages 0.14 and 0.02 respectively, whereas below it increases the chance of it being in class 3 with weightage 0.17. Here, the value of proline is 510, thus, increasing its probability of being in class 1.\n", + "\n", + "In a similar way, we can analyze each and every prediction in the dataset. Not much of a black box now, is it?" + ] + }, + { + "cell_type": "markdown", + "id": "96d6ebe6", + "metadata": { + "papermill": { + "duration": 0.074918, + "end_time": "2022-08-22T10:55:51.486725", + "exception": false, + "start_time": "2022-08-22T10:55:51.411807", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Explainability via SHAP" + ] + }, + { + "cell_type": "markdown", + "id": "20ee9cd5", + "metadata": { + "papermill": { + "duration": 0.074327, + "end_time": "2022-08-22T10:55:51.636131", + "exception": false, + "start_time": "2022-08-22T10:55:51.561804", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "SHapley Additive exPlanations (SHAP) - helps us interpret the contribution of each attribute on the prediction in a detailed manner. We'll go in the depth of it in the cells below. To begin, we first import shap." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "1d0570fa", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:55:51.791286Z", + "iopub.status.busy": "2022-08-22T10:55:51.790203Z", + "iopub.status.idle": "2022-08-22T10:55:55.178089Z", + "shell.execute_reply": "2022-08-22T10:55:55.176560Z" + }, + "papermill": { + "duration": 3.469036, + "end_time": "2022-08-22T10:55:55.181023", + "exception": false, + "start_time": "2022-08-22T10:55:51.711987", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# importing shap\n", + "import shap" + ] + }, + { + "cell_type": "markdown", + "id": "f50f77d9", + "metadata": { + "papermill": { + "duration": 0.075156, + "end_time": "2022-08-22T10:55:55.331876", + "exception": false, + "start_time": "2022-08-22T10:55:55.256720", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Next, we instantiate the TreeExplainer class, since our model was tree-based and pass our model to it." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "f3840388", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:55:55.485839Z", + "iopub.status.busy": "2022-08-22T10:55:55.485171Z", + "iopub.status.idle": "2022-08-22T10:55:55.508075Z", + "shell.execute_reply": "2022-08-22T10:55:55.506972Z" + }, + "papermill": { + "duration": 0.103727, + "end_time": "2022-08-22T10:55:55.510667", + "exception": false, + "start_time": "2022-08-22T10:55:55.406940", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# instantiating a TreeExplainer object\n", + "explainer = shap.TreeExplainer(model)" + ] + }, + { + "cell_type": "markdown", + "id": "23b4df8e", + "metadata": { + "papermill": { + "duration": 0.074899, + "end_time": "2022-08-22T10:55:55.662770", + "exception": false, + "start_time": "2022-08-22T10:55:55.587871", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "After that, we obtain shapely values (average expected marginal contribution of one player after all possible combinations have been considered) of our data using the shap_values attribute of the explainer object." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1408c1b9", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:55:55.817594Z", + "iopub.status.busy": "2022-08-22T10:55:55.817187Z", + "iopub.status.idle": "2022-08-22T10:55:55.874566Z", + "shell.execute_reply": "2022-08-22T10:55:55.873621Z" + }, + "papermill": { + "duration": 0.136517, + "end_time": "2022-08-22T10:55:55.877190", + "exception": false, + "start_time": "2022-08-22T10:55:55.740673", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# obtaining shapely values of the data\n", + "shap_values = explainer.shap_values(x)" + ] + }, + { + "cell_type": "markdown", + "id": "43ec7a1c", + "metadata": { + "papermill": { + "duration": 0.074193, + "end_time": "2022-08-22T10:55:56.027457", + "exception": false, + "start_time": "2022-08-22T10:55:55.953264", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Having obtained the shapley values, we plot the dependence between the shapely values and individual features using the dependence_plot() function from the shap library. Vertical dispersions at a single value reveal how certain traits interact with one another. For the purpose of making these interactions easier to see, SHAP automatically chooses a different feature for colouring." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f6f48ccd", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:55:56.180209Z", + "iopub.status.busy": "2022-08-22T10:55:56.179488Z", + "iopub.status.idle": "2022-08-22T10:55:56.511622Z", + "shell.execute_reply": "2022-08-22T10:55:56.510447Z" + }, + "papermill": { + "duration": 0.411955, + "end_time": "2022-08-22T10:55:56.514494", + "exception": false, + "start_time": "2022-08-22T10:55:56.102539", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the dependence of shapely values on alcohol\n", + "shap.dependence_plot('Alcohol', shap_values[0], x)" + ] + }, + { + "cell_type": "markdown", + "id": "aa15295d", + "metadata": { + "papermill": { + "duration": 0.089261, + "end_time": "2022-08-22T10:55:56.678059", + "exception": false, + "start_time": "2022-08-22T10:55:56.588798", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, the shapely values increase with increase in values of alcohol. Also, the flavanoid values are high at both the ends of alcohol values, whereas they are low in the middle values." + ] + }, + { + "cell_type": "markdown", + "id": "1fd8619b", + "metadata": { + "papermill": { + "duration": 0.073648, + "end_time": "2022-08-22T10:55:56.826960", + "exception": false, + "start_time": "2022-08-22T10:55:56.753312", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Next, we plot the dependence of shapely values on all the features using the summary_plot() function from the shap library." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "7571731c", + "metadata": { + "execution": { + "iopub.execute_input": "2022-08-22T10:55:56.979030Z", + "iopub.status.busy": "2022-08-22T10:55:56.978648Z", + "iopub.status.idle": "2022-08-22T10:55:57.697446Z", + "shell.execute_reply": "2022-08-22T10:55:57.696228Z" + }, + "papermill": { + "duration": 0.798061, + "end_time": "2022-08-22T10:55:57.700075", + "exception": false, + "start_time": "2022-08-22T10:55:56.902014", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the dependence of shapely values on all the features\n", + "shap.summary_plot(shap_values[0], x)" + ] + }, + { + "cell_type": "markdown", + "id": "1f15b0ac", + "metadata": { + "papermill": { + "duration": 0.074933, + "end_time": "2022-08-22T10:55:57.855146", + "exception": false, + "start_time": "2022-08-22T10:55:57.780213", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see the shape values:\n", + "- increase with increase in value of proline and cover a large range\n", + "- increase with increase in value of flavanoids and cover a medium range\n", + "- increase with increase in value of alcohol and cover a medium range\n", + "- increase with increase in value of diluted wines and cover a medium range\n", + "- increase with increase in value of color intensity and cover a medium range\n", + "- increase with increase in value of total phenols and cover a medium range\n", + "- increase with increase in value of hue and cover a small range\n", + "- increase with increase in value of magnesium and cover a small range\n", + "- decrease with increase in value of alcalinity of ash and cover a small range\n", + "- increase with increase in value of proanthocyanins and cover a small range\n", + "- are on the extremities for low values of malic acid and mid range for high values of malic acid and cover a small range\n", + "- increase with increase in value of ash and cover a small range" + ] + }, + { + "cell_type": "markdown", + "id": "c9300bf4", + "metadata": { + "papermill": { + "duration": 0.075638, + "end_time": "2022-08-22T10:55:58.006410", + "exception": false, + "start_time": "2022-08-22T10:55:57.930772", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "649b37fb", + "metadata": { + "papermill": { + "duration": 0.074327, + "end_time": "2022-08-22T10:55:58.156160", + "exception": false, + "start_time": "2022-08-22T10:55:58.081833", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Congratulations! You can now become uber-cool and explain what goes on in your model, isn't that exciting? We now encourage you to explore more functions in these liba" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + }, + "papermill": { + "default_parameters": {}, + "duration": 412.276501, + "end_time": "2022-08-22T10:55:59.357241", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2022-08-22T10:49:07.080740", + "version": "2.3.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Project-Structure.md b/Project-Structure.md index 164671843f..eaaddafd31 100644 --- a/Project-Structure.md +++ b/Project-Structure.md @@ -584,6 +584,8 @@ ## Explainable-Ai * Explainable Ai Research Methods * [Xai](Explainable-AI/Explainable%20AI%20Research%20Methods/xai.ipynb) + * Intro To Lime And Shap + * [Explainable-Ai-Intro-To-Lime-Shap](Explainable-AI/Intro_to_LIME_and_Shap/explainable-ai-intro-to-lime-shap.ipynb) * Xai On Cancer Dataset * [Cancer Dataset](Explainable-AI/XAI%20on%20Cancer%20Dataset/cancer_dataset.ipynb)