vinay-ghate / datascience-tasks

Data Science Tasks Showcase

This repository contains the code and results for a series of data science tasks performed using Python and Jupyter Notebooks.

Task 1: Exploring a Dataset

In Task 1, we selected a dataset from Kaggle and explored its basic characteristics. We used Python with libraries like Pandas to load the dataset, checked for missing values, and displayed summary statistics.

Task 2: Simple Data Visualization

Task 2 involved creating a basic bar chart or line chart using the Matplotlib library. We visualized key insights from the dataset to gain a better understanding of the data.

Task 3: Data Cleaning and Preprocessing

For Task 3, we selected a dataset with missing values and outliers. We applied techniques to clean and preprocess the data using Pandas, imputed missing values, and handled outliers appropriately.

Task 4: Predictive Modeling with Linear Regression

In Task 4, we implemented a simple linear regression model using a dataset with a clear linear relationship between variables. We used the Scikit-Learn library in Python for this predictive modeling task.

Task 5: Exploratory Data Analysis (EDA)

Task 5 involved performing a comprehensive exploratory data analysis on a dataset of our choice. We used visualizations and statistical measures to gain insights into the data's patterns and relationships.

Task 6: Classification with Random Forest

For Task 6, we built a classification model using a Random Forest algorithm on a dataset with categorical target variables. We evaluated the model's performance using metrics like accuracy and precision.

Task 7: Time Series Forecasting

Task 7 involved selecting a time-series dataset and implementing a forecasting model, such as ARIMA or Prophet, using Python. We visualized the predicted values and compared them with the actual data.

Task 8: Natural Language Processing (NLP)

In Task 8, we explored Natural Language Processing by analyzing text data. We used libraries like NLTK or SpaCy to perform tasks like sentiment analysis or text summarization.

Task 9: Advanced Feature Engineering

For Task 9, we chose a dataset and implemented advanced feature engineering techniques, such as creating interaction terms, polynomial features, or using domain-specific knowledge to enhance model performance.

Feel free to explore each task individually in the provided Jupyter Notebooks.