This repository contains data analysis programs in the Python programming language.
- Data Exploration: Intelligent data analysis involves thorough exploration of datasets to understand patterns, trends, and anomalies.
- Machine Learning Algorithms: It leverages machine learning techniques to build predictive models and extract insights from data.
- Decision Support: It assists decision-making processes by providing valuable insights and recommendations based on data analysis.
- Data Visualization: Intelligent data analysis often includes data visualization techniques to present findings in a more understandable and actionable format.
- Continuous Improvement: It is an iterative process, constantly refining models and analysis methods to improve accuracy and relevance.
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Demonstration of different Pre-processing Techniques including Missing Value Handling and Data Discretization on Election Dataset.
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Demonstration of Data Reduction Techniques including PCA and Histogram on Predict Students’ Dropout and Academic Success and Wine Quality Dataset.
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Demonstration of classification Rules Process on Dataset of Choice using ID3 and J48 Algorithm in Python.
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Implement the classification Rules Process on Dataset of Choice using Naive Baye’s Algorithm in Python.
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Build a Neural Network model to predict whether Tumor is Malignant or Benign for Breast Cancer Wisconsin (Diagnostic) Dataset using Python.
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Demonstration of Clustering on Dataset of Choice using Simple K-means Algorithm in Python.
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Demonstration of Clustering on Dataset of Choice using Simple DBSCAN Algorithm in Python.
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Demonstration of Clustering on Dataset of Choice using Simple BIRCH Algorithm in Python.
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Demonstration of Association Rule Generation on Groceries dataset for Market Basket Analysis using Apriori Algorithm in Python.
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Perform comparative analysis of Apriori and FP-Growth Algorithms on Market Basket Analysis usingPython.
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