Data Scientist
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A diligent business analyst and data enthusiast, good at transforming the data into business inference while building professional career.Proficient in data wrangling, statistical analysis,
and data visualization using tools like Python, SQL, and Tableau. Strong problem-solving abilities with a detail-oriented approach to deliver accurate and actionable results. Adept at communicating complex technical concepts to non-technical stakeholders.
My unique journey, from engineering to civil services preparation to project management and finally to entrepreneurship in the E-learning sector brought me an exposure to diversified fields
And in the process I realized my passion for data science and business analytics.Eager to apply my expertise in data science and Analytics to drive innovation and contribute to the success of your Organization.
Post-Graduate Program in Data Science and Business Analytics, 2023.DATA SCIENCE PROJECTS
Time Series Forecasting: Wine Sales Forecasting on analysing historical monthly sales data of a company.Created multiple forecast models for two different products and recommended the optimum forecasting model to analyse and forecast Wine Sales in the 20th century along with appropriate lower and upper confidence limits.Various forecasting models applied are :1.Linear Regression 2. Naive Bayes 3.
Single & Double Exponential Smoothing (Holt’s model) & Triple Exponential Smoothing (Holt-Winter
Model) 4. ARIMA / SARIMA (Auto fitted) & (Manually fitted), Made suggestions considering the business sales.
Machine Learning: Exit Polls and Text Analytics, Generated a model to create an exit poll that will help in predicting overall win and seats covered by the particular party. Tools Used: - NLP, EDA, KNN, Naïve
Bayes, Boosting, Bagging Found out that “Labour party is performing better than Conservative from huge margin”.
Predictive Modelling: A Contraceptive Prevalence Survey: To predict do/don't they use a contraceptive method of choice based on their demographic,socio-economic characteristics.
Making 3 models using Decision Tree Classifier Logistic Regression and LDA. After analysis the conclusion is that: People with very high standard of living index have used contraception more than the rest amongst other observations
Data visualization using Tableau: The Car Insurance Claims dataset. You have been given a task to explore the data, create different plots and interpret useful insights/findings and create a storyboard.
Market Basket Analysis: Customer Buying Patterns & Revenue Boost: Auto Parts & Grocery to find the underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company's data and hence recomend customized marketing strategies for different segments of customers and other part is providing recommendations through which a grocery store can increase its revenue by coming up with attractive combo & discounts.
