SUMMARY

Self-motivated and data-driven enthusiast with experience as an intern in data science and services. Gained hands-on experience in deploying statistical methods to analyze data and generate insightful business reports for top-level management and key stakeholders. KEY SKILLS

TECHNICAL SKILLS

EXPERIENCE

Data science Intern -

Flip Robo Technologies LLC

Flip Robo is an artificial intelligence company. We specialize in chats, web scrapping, and building algorithms. Data Analysis, Exploration & Process Optimization

Business Analyst -

P P Cottage Industries:

Responsible for Gathering, documenting, and analysing various business requirements. Retrieve and analyse data using SQL, Excel, Access and other data management systems. Analyse and report daily, weekly metrics, identify trends and opportunities to improve process.Data Analysis, Exploration & Process Optimization

EDUCATION

MBA -

Bharati VidyaPeeth Deemed University

Information technology and Project Management

Courses and Certificates: Python, SQL,Advanced Excel, Tableau, Machine Learning, Data Visualisation B.sc Computer-Science (2008-2011) Pune - University Pune PROJECTS

Data Science Capstone Project: IMDB Dataset

Flip Robo Technologies

Objective: Developed an ML Model for Analyze the movies data on the IMDb website between the year 1924 and the year 2020. Tech Stack: Selenium and data preprocessing(cleaning feature engineeering, and outlier detection) with Pandas, Numpy, Matplotlib, Seaborn Python, Machine learning algorithms, SQLite, Web Scraping using Selenium, Seaborn, Matplotlib for Data Visualisation, Normalization. Solution and conclusion: Analyzed the data,there are 128 missing values in Revenue column i.e. 12.8% which is right skewed, hence filling the same with median and predict the Grosscollection in this dataset.

GitHub Link: Phase1_2: 'https://github.com/RitaPadghan1234/Capston_Projects_DS0622/blob/main/Phase1_Phase%202%20(1).ipynb' Phase3_4: 'https://github.com/RitaPadghan1234/Capston_Projects_DS0622/blob/main/Phase_3_Phase_4%20(1).ipynb' Insurance Claim Fraud Detection

Flip Robo Technologies

Objective: To build a model that can predict whether the loan of the applicant will be approved(Loan_status) or not on the basis of the details provided in the dataset. Tech Stack: Pyhton, Machine Learning, Seaborn, Matplotlib Data Visualization Solution and conclusion: XGBoost had a more balanced performance. It found some importance in features like Married_Status, Dependents, Property_Area_Semiurban are features that are considered for taking the loan apprisal decision.Like other model shows Credit_History is the most important feature in making loan Prediction decision.

GitHubLink:'https://github.com/RitaPadghan1234/internship34_Repo_RitaPadghan/blob/1a877fc99aa420249baea07460ab044d42d68f2e/Evaluation_Project_Phas e3/Insurance%20Claim%20Fraud.ipynb'

Avocado Project:

Flip Robo Technologies

Objective: Developed an ML Model Retail scan data comes directly from retailers' cash registers based on actual retail sales of Hass avocados. Tech Stack: Pyhton, Machine Learning, Seaborn, Matplotlib Data Visualization, Random forest classification algorithm Solution and conclusion: Columns like type of avocado, size and bags have impact on Average Price, lesser the RMSE value accurate the model is, when we consider Small Hass in Small Bags. GitHub Link: 'internship34_Repo_RitaPadghan/EvaluationPhase_Projects/Phase 1/Phase1_Avocado_Project.ipynb at main · RitaPadghan1234/internship34_Repo_RitaPadghan (github.com)' ad2o47@r.postjobfree.com

Pune

Github: Rita Padghan

(91+) 959-***-****

linkedin: Rita

RITA PADGHAN

Data Analyst Enthusiast

• Data Analysis Data Mining Data Visualization Data Manipulation Data Extraction Report Generation Dashboard Management KPI Monitoring Regression and Segmentation Statistical Analysis Performance Tracking Advanced Excel Business Intelligence MS Word(Excel, Word, PowerPoint) MS Excel text functions

•

• Methodologies: SDLC, Agile, Waterfall

• Database: MySQL, SQL Server

• Data Science Toolkits/Packages: Scikit, Numpy, Pandas, NLTK, Beautiful Soup, Matplotlib, Seaborn, SciPy

• Programming Languages: Python, SQL

• Data Visualization: Tableau, MS Excel

• Statistics and Machine Learning: Classification, Regression, Decision Tree, Random Forest, Naive Bayes, KNN, K-means Nov '22 May '23

Bengaluru

• Completed data Preprocessing, cleaning, and wrangling to prepare data for modelling

• Gained experience with data visualization and exploratory data analysis

• Identified, analyzed, and interpreted trends in complex data sets using supervised and unsupervised learning techniques

• Contributed to the development of predictive models to solve business problems, such as customer churn and product demand forcasting

• Developed effective presentation and visualizations to communicate complex technical concept to non-technical stakeholders Apr '22 Present

Pune

Jul '21 Present

Pune

• Semester GPA: 9.5/10

• Semester GPA: 6.0/10

Contact this candidate