Jan 2024 – Jul 2024 · 6 mos

Aug 2022 – Dec 2023 · 1 yr 4 mos

May 2022 – Jun 2022 · 1 mo

• 1. Built a clustering model using K-means to identify major customer segments on demographic and psychographic-based data to optimize impact of marketing
• campaigns.
• 2. Engineered features to obtain new features such as RFM, RFMGroup, and RFMScore
• for getting more details about the customers' purchasing behaviour.
• 3. Evaluated the optimal clusters using Silhouette score and Elbow method and leveraged
• the visualization library t-SNE for multidimensional scaling to visualize and validate the
• inter-cluster separation and intra-cluster similarities.
• 4. Deployed K-Means clustering and got the optimal number of clusters equal to 2 having
• the highest silhouette score of 0.39.

May 2022 – Jun 2022 · 1 mo

• 1. Developed a robust book recommendation framework using memory and model based
• collaborative filtering by utilising the ratings of books and various user features.
• 2. Implemented Singular Value Decomposition based Matrix Factorization to obtain
• user-item interactions and employed cosine distance to measure user-item similarities.
• 3. Created profiles for top active users by leveraging interaction strength with the
• recommended items and achieved test recall@5 of 42% and recall@10 of 53%.
• 4. Handled the cold start problem based on global and demographic-specific book
• popularity and improved the efficiency of the user recommendation engine by 33%.

Apr 2022 – May 2022 · 1 mo

• 1. Developed a binary classification model using algorithms such as Logistic Regression, SVC and XGBoost to predict whether a customer will default on credit card payments.
• 2. Extraction of features from the customers datasets followed by data analysis using
• Statistical and Visualization tools.
• 3. Implemented SMOTE boosting to oversample the minority class observations and
• carried out hyperparameter tuning.
• 4. Deployed XGBoost classifier to predict whether a customer will default and achieved
• highest test accuracy score of 87% and AUC is 0.873.

Mar 2022 – Apr 2022 · 1 mo

• 1. Developed a regression model using algorithms such as Random Forest and XGBoost
• to predict the hourly demand of bikes and reduced the public waiting time.
• 2. Designed the data pipeline to work on, understood the impact of features such as time of the day, weather condition, holidays and engineered important features for model.
• 3. Imputed missing values, encoded categorical columns, handled outliers, checked multicollinearity and experimented with different models.
• 4. Performed hyperparameter tuning techniques such as GridSearch CV and achieved R2
• score of 92% using XGBoost model and reduced the public waiting time significantly.

Feb 2022 – Aug 2022 · 6 mos