Project 4: Prediction of Student's Dropout and Academic Success

Implemented Machine learning techniques such as Logistic Regression, Decision trees, Random forest,and K-nearest neighbors (KNN) were used to predict student dropout and academic success in a project. Preprocessed the data through encoding categorical variables, dropping irrelevant columns, and splitting into training and testing sets. Conducted Exploratory Data Analysis to determine the relationship between features and the target variable, and scaled the data using min-max scaling. Evaluated the model performance using accuracy, precision, and recall.

November 3, 2022

Project 5: Prediction of Customer Churn of a Telecommunications Company

Sourced and prepared data for analysis with data preparation done to ensure validity, accuracy, completeness, consistency, and uniformity. Performed feature selection and engineering i.e., label and one-hot encoding, SMOTE, normalization, and train-test splitting with a 0.25 test size and random state of 1. Employed modelling techniques such as ADA Boost, Random Forest, SVM, Lasso Regression, Naive Bayes, and Logistic Regression, with the Random Forest model performing best with a score of 92%.

October 18, 2022

Project 6: Spam Classfication using NLP and Naive Baiyes

Preprocessed the text data to extract relevant features, including word frequencies and document frequency. Implemented Naive Bayes and Natural Language Processing algorithms to classify the messages as spam or not spam based on these features. The model achieved high accuracy in identifying spam messages. The project is useful for email spam filtering and other text classification tasks. The project demonstrates the effectiveness of machine learning and NLP techniques in addressing real-world problems.