https://github.com/guntas-13/data-narrative_tennis-major-tournaments

This repo presents forth a Data Narrative prepared as a part of a course project. The data analysis involves the use of Clustering, Classfication, and Regression using machine learning models in python's Scikit-Learn Library. The repo also includes a written report for the same as compiled in LaTex.
https://github.com/guntas-13/data-narrative_tennis-major-tournaments

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This repo presents forth a Data Narrative prepared as a part of a course project. The data analysis involves the use of Clustering, Classfication, and Regression using machine learning models in python's Scikit-Learn Library. The repo also includes a written report for the same as compiled in LaTex.

• Host: GitHub
• URL: https://github.com/guntas-13/data-narrative_tennis-major-tournaments
• Owner: guntas-13
• Created: 2023-05-03T06:42:38.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-05-12T20:42:16.000Z (about 2 years ago)
• Last Synced: 2025-01-30T17:39:06.258Z (4 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 1.76 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Data Narrative: Tennis Major Tournaments Dataset

This repo presents forth a Data Narrative prepared as a part of a course project. The data analysis involves the use of **Clustering, Classfication**, and **Regression** using **machine learning models** in python's **Scikit-Learn** Library. The repo also includes a written report for the same as compiled in **LaTex**.

## The Overview

The Narrative was part of the **ES-114: Probability, Statistics, and Data Visualization** course offered to the first-year B.Tech students during the **April '23** for the first time at the **Indian Institute of Technology, Gandhinagar.**

The Machine Learning Course **ES-113: Data Centric Computing** here at **IIT, Gandhinagar,** was conducted in coherence with this course.

The Narrative allowed for the analysis of the [Tennis Major Tournaments](https://doi.org/10.24432/C54C7K) dataset using **Clustering, Classification,** and **Regression**. Several of Probabilistic and Data Analystic tools like **Covariance Matrices, Correlation Factors,** and **Principal Components Analysis** were employed in the narrative.

## Libraries used and Analysis Approach

The following is the list of Python Libraries used for the narrative:

1. **Pandas**

2. **NumPy**

3. **Scikit-Learn**

> * sklearn.naive_bayes.GaussianNB

> * sklearn.preprocessing.StandardScaler

> * sklearn.decomposition.PCA

> * sklearn.cluster.KMeans

> * sklearn.metrics.accuracy_score

> * sklearn.model_selection.train_test_split

> * sklearn.model_selection.cross_val_score

4. **Plotly**

> * plotly.express.scatter()

> * plotly.express.box()

> * plotly.express.3d_scatter()

> * plotly.express.bar()

* The main aim of the analysis was to predict the target (win: 0/1) of the newer result based upon the features given to us to work with.

* To prepare our data for training, number of features had to be reduced down.

* Correlation among the features was analysed and redundant features having high correlation were shot-down.

* Now with the less features, the data was prepared for training on a **Gaussian Naive Bayes classifier**.

* Accuracy of this model was tested using a 5-fold cross validation score. The model actually gave a **high accuracy score of 0.9049230769230769**.

* Another way of unsupervised learning was the use of **KMeans clustering**.

* Two clusters were allowed to form and the accuracy was compared with original data.

* To visualize the clustering, the dimensionality of the features was reduced to 3 using **Principal Component Analysis (PCA)** so clusters were plotted in 3D.

* Finally, there were some ways using Pandas and NumPy to filter out important statistical data/stats and the same was plotted for using Plotly graphs.

Please refer to the [Colab link](https://colab.research.google.com/drive/1Z0mJhQlPfRp8m01fKYLkdour3K9RcWca?usp=sharing) to view the interactive Plotly graphs.