sumitkutty / Sparks-Foundation-Internship

The projects are a part of the internship by The Sparks Foundation

Sparks-Foundation-Internship

These projects are a part of the internship by The Sparks Foundation

Basic implementation of some machine learning algorithms.

Project 1

Objective:

• Exploratory data analysis on the global terrorism dataset. This project involved creating multiple graphs while deriving elborative insights to find hot spots and other security issues.
• link: https://github.com/sumitkutty/Terrorism-Analysis

Project 2: Prediction using Supervised ML.

Objective:

• This project involved predicting the score of a student gives the number of hours studied. This is simple linear regression problem with one dependant and independant variable. The objective also included finding the score for 9.25 hrs/day study. The evaluations were done using k-cross validation (5 folds).

Packages:

• Scikit-Learn, Pandas, Numpy, Matplotlib, Seaborn.
• Metrics Used: R-Squared Score,Mean Squared Error, Mean Absolute Error, Root Mean Squared Error.
  

Evaluation:

• Average r2 score: 0.9180771341891051
  
• Average MAE score: -5.701180786326049
  
• Average MSE score: -38.4709444297968
  
• Average RMSE score: -5.970137529278324
  

Project 3: Prediction using Unsupervised ML.

Data: Iris Dataset

Objectives:

• Involves paritioning the data into clusters with similar characteristics. The objective also involves finding the optimal no of clusters to partion the data into.

Methods used:

1. Elbow Method:

• This method is used to find the no of clusters which would be optimum.
• It is a method used a find the elbow point in the graph where the wcss is plotted against different k values.
• WCSS(within cluster sum of squares) is the avg distance between the datapoints and their respective centroids.
• so by definition, lower the WCSS, closer the datapoints are to their to centroids, better the clustering.

2. KMeans Algorithm:

• Kmeans is an unsupervised learning method which partitions the data into k clusters based on the data's characters.
• k value here is the value found by the elbow method.

Packages Used:

• Numpy, Pandas, matplotlib, seaborn, scikit-learn

Evaluation:

• WCSS value: 78.94

Project 4: Prediction using Decision Tree Algorithm

Data: Iris Dataset

Objectives:

• The objective of the project was to construct a decision tree and visualize it.
• The decision tree classifier should be able to classify a custom data point.
• Visualizing the tree graphically.

Methods:

1. EDA:

• Plotted a number of graphs denoting the distributions and the relations between the features

2. Cross Validation:

• Performed cross validation with 5 folds to test the accuracy of the classifier.

3. Hyperparameter Tuning:

• Tuned the model to find the optimum hyperparameters.

4. Modelling:

• Fit the optimal hyperparameters found on the test set.

5. Tree plot

• Plotted the tree using the plot_tree function from the tree class.

Evaluation:

• Accuracy before tuning (cross validation): 0.94
• Accuracy after tuning: 0.9733
• Accuracy on the test set: 1.0

THE END