Complete Explanation For Data Science Resume Projects

Description

I want help in explaining my data science projects in interview in a detailed manner .
I am data science professional looking for a job switch of 3 years.
Projects:
1) CONSUMER COMPLAINT CLASSIFICATION (NLP) Building an API and training model to classify future complaints based on its content for a banking firm. The dataset is of 2 million rows, 5+ years historical data. Skills/Technology: Python, SVM classification, Random Forest ,Flask, Glove ,Word2Vec
2) PREDICTIVE MAINTENANCEBuilt an end to end machine learning model for a heavy industry firm which records different features like power,temperature etc for machines and predicting whether failure will happen or not.Skill/Technology: Python, FastAPI, Digital Ocean.,Streamlit,Docker3) RECOMMENDATION ENGINE FOR AUTOMATED TRADING PLATFORMDeveloped custom Technical Indicators Functions to analyse historical price data and market trends, triggering buying signals when specific conditions were met. Empowered traders clients with actionable insights by providing timely buy signals aligned with market trends. Skill/Technology: TA-Lib, TensorFlow, NumPy, Pandas, sklearn, statsmodels
4) INSURANCE POLICY CROSS SELLINGA classification/ranking project aimed to detect health insurance customers most likely to buy a new type of insurance from the company - car insurance. To solve this problem a machine learning model was built to classify the customers by their probability of buying the insurance. The Heroku platform was used to deploy the ML model, which will respond to requests via API. Skill/Technology: XGBoost,LightGBM, NumPy, Flask,Heroku
5) CUSTOMER CHURN PREDICTION FOR A MALAYSIAN BANKDeveloped a model to analyse customer data and predict churn to boost customer retention.Employed statistical techniques on customer data using Pandas, Seaborn, and Sklearn. Reduced customer churn rate by 7%, leading to increased revenue, lower marketing costs, and enhanced customer loyalty.