Olympic Medal Prediction

Goal

The goal of this project is to make a prediction model which will predict whether an athlete will win medal or not.

DATASET

The dataset which is used in this project, is collected from Kaggle. Here is the link of the dataset : Click here

LIBRARIES NEEDED

• Pandas
• Numpy
• Sklearn
• Matplotlib

WHAT I HAD DONE

1. Importing all the required libraries. Check requirements.txt
2. Upload the dataset and the Jupyter Notebook file.
3. Loading and Cleaning the dataset.
4. Encoding textual columns.
5. Select best 10 features and make a new dataframe using them
6. Split dataframe into train and test data along with scaling.
7. Apply different classification models and calculate their accuracy.
8. Choose best model for our prediction which has highest accuracy.
9. Make the final prediction using best model.

Model Comparison

We have deployed 6 machine learning algorithms and every algorithm is deployed successfully without any hesitation. We have checked the efficiency of the models based on the accuracy of each of the models. Now let's take a look at model with their accuracy.

Name of the Model	Accuracy
Logistic Regression	85.357825
MultinomialNB	85.367968
Decision Tree	84.095457
Random Forest	88.779773
Gradient Boosting	88.514205
Neural Network	87.904691

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Conclusion

Comparing all those scores scored by the machine learning algorithms, it is clear that Random Forest Classifier Model gives highest accuracy and will provide best prediction.

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Author :

Hey, This is Hrugved Kolhe.

_{Hrugved Kolhe}

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