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diff --git a/...etection with CNN and EfficientNetB3/blood-cell-cancer-using-cnn-and-efficientnetb3.ipynb b/...etection with CNN and EfficientNetB3/blood-cell-cancer-using-cnn-and-efficientnetb3.ipynb
diff --git a/Body Fat Prediction/BodyFatPrediction.ipynb b/Body Fat Prediction/BodyFatPrediction.ipynb
diff --git a/Body Fat Prediction/Feature_Importance_score.ipynb b/Body Fat Prediction/Feature_Importance_score.ipynb
diff --git a/Body Fat Prediction/Feature_importance.md b/Body Fat Prediction/Feature_importance.md
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+# Feature Importance Analysis using Chi-squared, and ANOVA
+
+## Introduction
+In this analysis, we explore the importance of features in predicting body density. We use Principal Component Analysis (PCA) to understand the variance explained by the features and Chi-squared and ANOVA tests to rank the features based on their importance contribution towards the label.
+
+## Feature Importance Ranking
+We utilized Chi-squared and ANOVA tests to rank the features based on their importance contribution towards predicting body density.
+
+### Chi-squared Test
+- The Chi-squared test is used to determine whether there is a significant association between categorical variables.
+-  Target variable, 'Density', appears to be continuous, the Chi-squared test might not be applicable to it directly. However, if we discretize the target variable into bins or categories, we can use the Chi-squared test to analyze its relationship with other categorical features in our dataset.
+
+
+### ANOVA Test
+- ANOVA is used to compare the means of three or more groups to determine if they are significantly different from each other. It assesses whether there are statistically significant differences among group means.
+- ANOVA can be applied to assess whether there are significant differences in the means of our continuous features ('Weight', 'Age', 'Height', etc.) across different categories of our target variable, 'Density'.
+
+## Conclusion
+- the Chi-squared test is suitable for analyzing relationships between categorical variables, while ANOVA is suitable for comparing means across different groups, especially when dealing with continuous and categorical variables. 
+- we can conclude that 'BodyFat', 'Weight', and 'Abdomen' are the most important features for predicting body density, followed by 'Age', 'Chest', 'Hip', 'Thigh', 'Biceps', 'Knee', 'Neck', and 'Forearm'. 'Ankle', 'Height', and 'Wrist' show weaker associations with the target variable and may have less predictive power in this context
diff --git a/Body Fat Prediction/Feature_importance_analysis.ipynb b/Body Fat Prediction/Feature_importance_analysis.ipynb
diff --git a/Body Fat Prediction/README.md b/Body Fat Prediction/README.md
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+# Body Fat Prediction Dataset 
+
+Welcome to the Body Fat Prediction Dataset! This dataset contains estimates of the percentage of body fat determined by underwater weighing and various body circumference measurements for 252 men. 
+
+- [Body Fat Prediction Dataset](https://www.kaggle.com/datasets/fedesoriano/body-fat-prediction-dataset)
+
+## Motivation 💡
+
+Accurate measurement of body fat is inconvenient and costly. Thus, it's desirable to have easy methods of estimating body fat that are not inconvenient or costly. This dataset can be used to illustrate multiple regression techniques for predicting body fat percentage using easily measurable body circumference measurements.
+
+## Dataset Details 📊
+
+The dataset includes the following variables:
+
+- Density determined from underwater weighing
+- Percent body fat from Siri's equation
+- Age (years)
+- Weight (lbs)
+- Height (inches)
+- Various body circumference measurements: Neck, Chest, Abdomen 2, Hip, Thigh, Knee, Ankle, Biceps, Forearm, and Wrist
+
+These measurements follow specific standards outlined in Behnke and Wilmore (1974).
+
+## Educational Use 📚
+
+This dataset is ideal for educational purposes, particularly for illustrating multiple regression techniques in data analysis and machine learning. By utilizing the provided features, one can develop predictive models to estimate body fat percentage using simple measurement techniques.
+
diff --git a/Body Fat Prediction/bodyfat.csv b/Body Fat Prediction/bodyfat.csv
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -22,8 +22,55 @@ in case you are stuck:
 - [Getting started with Git and GitHub](https://towardsdatascience.com/getting-started-with-git-and-github-6fcd0f2d4ac6)
 - [Learn GitHub from Scratch](https://docs.github.com/en/get-started/start-your-journey/git-and-github-learning-resources)
 
+
+### Alternatively contribute using GitHub Desktop
+
+1. **Open GitHub Desktop:**
+   Launch GitHub Desktop and log in to your GitHub account if you haven't already.
+
+2. **Clone the Repository:**
+   - If you haven't cloned the ThereForYou repository yet, you can do so by clicking on the "File" menu and selecting "Clone Repository."
+   - Choose the ThereForYou repository from the list of repositories on GitHub and clone it to your local machine.
+
+3. **Switch to the Correct Branch:**
+   - Ensure you are on the branch that you want to submit a pull request for.
+   - If you need to switch branches, you can do so by clicking on the "Current Branch" dropdown menu and selecting the desired branch.
+
+4. **Make Changes:**
+   Make your changes to the code or files in the repository using your preferred code editor.
+
+5. **Commit Changes:**
+   - In GitHub Desktop, you'll see a list of the files you've changed. Check the box next to each file you want to include in the commit.
+   - Enter a summary and description for your changes in the "Summary" and "Description" fields, respectively. Click the "Commit to <branch-name>" button to commit your changes to the local branch.
+
+6. **Push Changes to GitHub:**
+   After committing your changes, click the "Push origin" button in the top right corner of GitHub Desktop to push your changes to your forked repository on GitHub.
+
+7. **Create a Pull Request:**
+  - Go to the GitHub website and navigate to your fork of the ThereForYou repository.
+  - You should see a button to "Compare & pull request" between your fork and the original repository. Click on it.
+
+8. **Review and Submit:**
+   - On the pull request page, review your changes and add any additional information, such as a title and description, that you want to include with your pull request.
+   - Once you're satisfied, click the "Create pull request" button to submit your pull request.
+
+9. **Wait for Review:**
+    Your pull request will now be available for review by the project maintainers. They may provide feedback or ask for changes before merging your pull request into the main branch of the ThereForYou repository.
+
+## 📈 Development Workflow
+
+When working on the project, please follow these guidelines:
+
+1. Always work on a new branch for each separate issue or feature.
+2. Keep your branch up to date with the main repository's `master` branch.
+3. Write clear and descriptive commit messages.
+4. Test your changes thoroughly before submitting a pull request.
+5. Keep discussions polite and respectful.
+
+
 <br>
 
+
 ## **Issue Report Process 📌**
 
 1. Go to the project's issues.