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| 1 | + Predicting Income using Random Forest Classifier and Exploratory Data Analysis of the income dataset: |
| 2 | + |
| 3 | + Description: |
| 4 | + This project encompasses exploratory data analysis and predictive modelling for income classification using a Random Forest Classifier. |
| 5 | + The dataset, loaded from a CSV file, contains diverse socio-economic features.It employs advanced visualizations, including a correlation network, to uncover intricate feature relationships. After preprocessing and encoding, |
| 6 | + a Random Forest Classifier predicts income levels with a focus on accuracy and classification metrics. |
| 7 | + |
| 8 | + EDA Graphs: |
| 9 | + |
| 10 | +Correlation Network |
| 11 | +  |
| 12 | + |
| 13 | +Pairplot: |
| 14 | + |
| 15 | + |
| 16 | +Stripplot |
| 17 | + |
| 18 | + |
| 19 | +Box-plot |
| 20 | + |
| 21 | + |
| 22 | +ViolinPlot |
| 23 | + |
| 24 | + |
| 25 | +KDE |
| 26 | + |
| 27 | + |
| 28 | +Correlation Matrix |
| 29 | + |
| 30 | + |
| 31 | +Model: Random Forest Classifier: |
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| 33 | + |
| 34 | + |
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