Data Analysis Machine Learning

MOHANA KN

***************@*****.*** 827-***-**** Bangalore, Karnataka

EXPERIENCE

Bosch Limited. 06/2021 – Present

Data analyst Bangalore

Having 3 years of experience in data analysis, data cleaning, data mining, predictive machine learning algorithm, Structured query language (SQL) and Statistical techniques.

Good experience in exploratory data analysis Process to convert unstructured data into structured meaning full data.

Development of live data monitoring dashboards for data analysis and understand insights, patterns, and trends using Power BI and Tableau.

Creating report and presentations for clints or internal departments based on data analysis.

Good knowledge in python libraries like pandas, NumPy, mat plot lib, seaborn Scikit-learn.

Develop machine learning algorithm decision tree, random forest, regression model and clustering to predict parameters result using input data.

Writing SQL query and extract data from azure data bricks or MS SQL for data analysis and find root cause for problem solving.

Statistical analysis using hypothesis testing to reduce random testing frequency. EDUCATION

Bachelor’s degree from RR college of engineering Bangalore Aug-2017 – Aug 2020 SKILLS

Technical skills: Python, Data analytical and data visualizations, Tableau, Power BI, Machine learning algorithm, Statistics, hypothesis testing, SQL, Pandas, NumPy, Scikit-learn, Clustering and regression, Excel, Forecasting, Mathematics.

Soft skills: Problem solving, Communication Skills, Teamwork and collaboration, Creative thinking, Time management.

Language: Kannada (Native), English (fluent).

CERTIFICATIONS

Data science: Certified from Steinbeis university Germany.

Tableau and power BI

ACHIEVEMENTS

Awarded for developing most number of live dashboards.

Selected as a data enthusiast in the year of 2023 and 2024. PROJECTS

Cost reduction project using data analysis:

Detailed data analysis for leakage parameters with respect to increase first pass yield.

Developed interactive dashboard and forecasted for next six months with intervention limit to get alert if value reaches above limit.

Alert will help to prevent the problem and get resolved before occurrence and saving up to 50% of actual cost.

KPI: Cost

Tools used: MS SQL, Tableau, Forecasting, Python.

Improve productivity and OEE using data analysis:

Data collection by writing query and get data from azure data bricks.

Perform exploratory data analysis(EDA) to convert meaningful data

Create calculative measure using data analysis expressions (DAX) for better insights.

Find out root cause and Fix damaged vacuum instruments and created live dashboard to monitor leakage to avoid future rejection, resulting 10% OEE improvement. KPI: Overall efficiency and FPY.

Tools used: Tableau, Azure data bricks, Forecasting technique. Statistical data analysis:

Hypothesis method to reduce random testing frequency with help of historical data.

Define null and alternative hypothesis based on proposal and technically reject null hypothesis.

Resulting 30% of actual cost saving without affecting quality. KPI: Cost

Tools used: Statistical technology, Power BI.

Predictive machine learning project:

Perform EDA steps like remove duplicated and irrelevant data, handling null values, analyze distribution data etc...

Develop multiple models and select based on accuracy with help of confusion matrix.

Develop machine learning algorithm to predict machine result using previous machine data and increase overall efficiency and cost saving by 5%. KPI: Productivity, 5% cost saving.

Tools used: ML model-Decision tree, Python, EDA, Matplot lib, Jupiter notebook, Azure data bricks.

Contact this candidate