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https://github.com/hatimloha/prodigy_infotech

Data Science Project
https://github.com/hatimloha/prodigy_infotech

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Data Science Project

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README 

        
_**Company Name: Prodigy_InfoTech**_



_**Data Science Project**_



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In this project, we undertake various data mining tasks to analyze and visualize data. The tasks include:

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# Task 1:

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Create a bar chart or histogram to visualize the distribution of a categorical or continuous variable, such as the distribution of ages or genders in a population.

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# Task 2:

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Perform data cleaning and exploratory data analysis (EDA) on a dataset of your choice, such as the Titanic dataset from Kaggle. Explore the relationships between variables and identify patterns and trends in the data.

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# Task 3:

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Build a decision tree classifier to predict whether a customer will purchase a product or service based on their demographic and behavioral data. Use a dataset such as the Bank Marketing dataset from the UCI Machine Learning Repository.

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# Task 4:

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Analyze and visualize sentiment patterns in social media data to understand public opinion and attitudes towards specific topics or brands.

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# Task 5:

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Analyze traffic accident data to identify patterns related to road conditions, weather, and time of day. Visualize accident hotspots and contributing factors.

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# Tools Used in Project

Tool Use: [Google Colab](https://colab.research.google.com)

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All these tasks were completed using Google Colab. Google Colab provided a powerful and flexible environment for executing our data analysis and visualization processes. With its cloud-based resources, we could efficiently run complex computations and leverage its collaborative features to work seamlessly as a team. The integration of popular data science libraries and the ability to share notebooks easily made Google Colab an ideal platform for completing our data mining tasks.

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# Libraries

## In this project, we utilized several powerful libraries to perform data analysis and visualization:

1. matplotlib.pyplot (plt):

> Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. The pyplot module provides a MATLAB-like interface for plotting.



2. NumPy:

> NumPy is a fundamental package for numerical computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.



3. pandas:

> Pandas is a versatile library for data manipulation and analysis. It offers data structures like DataFrames, which make it easy to handle and analyze structured data.



4. seaborn:

> Seaborn is a statistical data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.



5. %matplotlib inline:

> This magic command is used in Jupyter notebooks to display Matplotlib plots inline, directly below the code cells that produce them. It is particularly useful for interactive data exploration.



6. scikit-learn (sklearn):

> Scikit-learn is a robust machine learning library for Python. It features various classification, regression, and clustering algorithms, and is designed to work seamlessly with NumPy and pandas.



7. TextBlob:

> TextBlob is a library for processing textual data. It provides simple APIs for common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.