Sai Sindhura

Entry-level role in Data Analytics, AI/ML, or Software Engineering

Bengaluru, Karnataka 0+ yrs exp 80 · Excellent

About

Computer Science Engineering graduate (AI-ML specialization, 8.5 CGPA) with hands-on experience in machine learning, data analysis, and software development. Proficient in Python, SQL, Streamlit, and Power BI, with experience building regression, classification, and LSTM models, preprocessing and analyzing data, and developing interactive applications. Strong analytical, problem-solving, and collaboration skills with a passion for transforming data into actionable insights.

Skills & Expertise (24)

Python Intermediate

7.5/10

Years Exp

SQL Intermediate

6.5/10

Years Exp

Pandas Intermediate

6.5/10

Years Exp

Logistic Regression Intermediate

6.5/10

Years Exp

Linear Regression Intermediate

6.5/10

Years Exp

Visual Studio Code Intermediate

6.0/10

Years Exp

Random Forest Intermediate

6.0/10

Years Exp

GitHub Intermediate

6.0/10

Years Exp

Natural Language Processing Intermediate

6.0/10

Years Exp

Problem Solving Intermediate

6.0/10

Years Exp

Model Evaluation Intermediate

6.0/10

Years Exp

scikit-learn Intermediate

6.0/10

Years Exp

Microsoft Excel Intermediate

6.0/10

Years Exp

Power BI Intermediate

5.5/10

Years Exp

LSTM Intermediate

5.5/10

Years Exp

TensorFlow Intermediate

5.5/10

Years Exp

Keras Intermediate

5.0/10

Years Exp

DAX Intermediate

5.0/10

Years Exp

Power Query Intermediate

5.0/10

Years Exp

Data Structures & Algorithms Intermediate

5.0/10

Years Exp

Object-Oriented Programming Intermediate

5.0/10

Years Exp

NLTK Intermediate

5.0/10

Years Exp

Jira Intermediate

5.0/10

Years Exp

Streamlit Beginner

4.5/10

Work Experience

Machine Learning Intern

Codtech IT Solutions

Jun 2024 - Jul 2024

Developed and evaluated supervised machine learning models, including Linear Regression, Logistic Regression, Random Forest, and LSTM, for house price prediction and sentiment classification using real-world datasets. Performed data preprocessing, feature engineering, dataset splitting, and NLP text preprocessing techniques, including tokenization, stemming, and stop-word removal, to improve model performance. Achieved 89% accuracy in sentiment classification using Logistic Regression and evaluated model performance using appropriate classification metrics.