Goal We aim to explore the options when we have highly skewed dataset to prepare the model and best performance meteric to be used for evaluation and prepare a model comparison report. Work Plan: What performance metric we should choose and Why? How to deal with highly skewed dataset - Undersampling , Oversampling , SMOTE Perform Precision-Recall trade off Go to Github source code

Overview We need to understand our customer’s purchase behaviour and rank them on the basis of how much they spend, how frequent they visit and how recent they visited. Research shown: Cohort Analysis Customer Retention Recency Frequency Monetary Model (RFM) EDA Elbow method for Optimum clusters Go to Github source code

Overview We aim to build recommender System to provide Statistical understanding for movie rating dataset and better cinema to customers. Research shown: Exploratory Data Analysis Feature Selection - Chi-square test/Hypothesis testing Go to Github source code

Overview We aim to understand the customer complaints and its resolution time & Feature responsible for reolution time. Research shown: Exploratory Data Analysis *Hypothesis Testing & Chi-square testing Go to Github source code

Overview We aim to explore the dataset and get insights on the feature impacting the employee to leave the organization. And build a regression model to predict their attrition rate. Research shown: Explrotary Data Analysis Interaction Terms involved Regression Model Go to Github source code