Results-driven Lead Data Analyst with expertise in transforming complex raw data into actionable insights using SQL and Python. Skilled in developing dynamic Tableau and Power BI dashboards to drive data-driven decision-making. Known for uncovering key data trends and developing innovative solutions to solve business problems.
Snowflake
SQL Expertise
Python Programming
ETL development
Microsoft Power BI
Microsoft Excel
Tableau
Sigma Computing
GitHub
Docker
Machine learning Algorithms/Models
Data Quality Management
Data cleansing
Data Mining
Exploratory Data Analysis
Feature Engineering
Predictive model
Pandas
NumPy
Matplotlib
Sklearn
TensorFlow
Keras
BeautifulSoup
Selenium
Requests
Scrapy
Flask
Classification
Regression
K-Means Clustering
Predictive Analysis
Hypothesis
Basket Analysis
Text Analytics
R
Tableau
Advanced Excel
MYSQL
POSTGRESQL
Missing value & Outlier Value treatment
Visualizations
One hot encoding
Hyper parameter Tuning
Lasso
Ridge
Elastic Net Regression
Support Vector Regression
Gradient Boosting Regression
LGBM
XGBoost
Capstone project in Supply Chain Management domain:
Obtained material purchasing data for a real time manufacturing company. Identified their monthly material usage. Forecasted material usage by applying Machine Learning with Hierarchical Project Approach: Data Cleansing | Missing Value | Outlier Treatment | Exploratory Data Analysis | Feature Engineering | Model Building |Hyperparameter Tuning | Model Validation | Forecast | Time Series. Later, suggested safety stock levels to be maintained by the company. Snowflake, MYSQL, MS SQL, POSTGRESQL properties of sale and factors affecting it. Tableau’s data visualization tools were used to create Models: HTS Forecasting: ARIMA, SNAIVE, ETS, NNETAR, XGBOOST, CATBOOST, LightGBM. Based on model results, final forecasts were made using catboost model. The models are validated using the metrics: MAPE, MAE, RMSE, SMAPE.
Salary Prediction using ML & deploying the model in Heroku (End to End):
Data cleansing | EDA | Feature Engineering | Predictive model using XGBoost | Deploying the model on Heroku using Flask API.
House Price : Advanced Regression Techniques:
Date Cleaning | Missing value & Outlier Value treatment | Exploratory Data Analysis &visualizations| One hot encoding | Feature Engineering | Hyper parameter Tuning | Lasso, Ridge, Elastic Net Regression, Support Vector Regression, Gradient Boosting Regression, LGBM, XGBoost, Bagging & Stacking regressor.
Web scrapping with Python:
Web scrapped online bookstore using python - Selenium | Requests | BeautifulSoup | Scrapy | xml.
Factor Analysis on Consumer Perception towards Cereal Brands:
Consumer perceptions towards a brand can be built on various parameters depending upon the product. In this project, I have used Factor Analysis to evaluate and identify the most significant attributes that contribute towards the consumer perception of the different cereal brands.
Visualizing House Sales for Boston Real Estate in Tableau:
In this project, I have explored the art of problem-solving with the aid of visual analytics to identify properties of sale and factors affecting it. Tableau’s data visualization tools were used to create interactive dashboards to uncover hidden insights for sellers of the properties as well as prospective buyers. https://public.tableau.com/profile/prasanna3111#!/vizhome/BostonCondostorybyPrasanna/BostonCondo-StorybyPrasanna
Evaluated impact of change in temperature of Cold Storage products during the year:
In this project, I have computed the mean cold storage temperature in different seasons and used hypothesis testing to evaluate if any corrective action is required from the plant's side or the procurement's side.
Evaluated and inferred significant predictors affecting the customer loan preferences:
In this project, I have found Education and Income are the most significant predictors affecting the customer response on seeking personal loan for ‘Thera bank’. Business strategies can be made by concentrating more on these variables to increase the potential customers seeking personal loan.
Created multiple dashboards using Power BI:
Performed ETL & data visualization using Power BI | Extracted data to power BI by connecting to multiple Microsoft office tools, SQL | Transformed data using Power Query | Performed Data Modelling using DAX functions, measures, mfunction and connecting multiple tables. My first blog on power BI is featured by Analytics-vidhya in their medium publication. https://medium.com/analytics-vidhya/a-complete-beginners-guide-on-power-bi-ed7d54a0b73e