Based on the loan_approval_dataset.csv. Write a Jupyter Notebook that applies the following Tasks: 1. Data Preprocessing: Clean and preprocess the selected dataset to handle missing values, outliers, and any other data quality issues. Perform exploratory data analysis (EDA) to gain insights into the data. 2. Feature Engineering: Create relevant features that can aid in solving the chosen problem. This may involve transforming variables, creating new features, or selecting the most important ones. 3. Model Selection: Experiment with different machine learning algorithms, such as classification, regression, clustering, or recommendation systems, depending on the nature of the problem. Evaluate the performance of these models using appropriate metrics. 4. Hyperparameter Tuning: Fine-tune the selected models by optimizing hyperparameters using techniques such as grid search or random search. 5. Model Evaluation: Compare the performance of different models and choose the best-performing one based on relevant evaluation metrics. Explain why this model was chosen. 6. Interpretability: If possible, provide insights into the factors influencing the chosen problem by interpreting the model. 7. Validation: Split the dataset into training and testing sets to assess the model's generalization performance. Use cross-validation techniques to ensure robustness. 8. Conclusion and Future Work: Discuss the implications of the project's findings and insights. Suggest potential future improvements or extensions to the project. AutoSave Off loan approval_dataset Search File Home Insert Page Layout Formulas Data Review View Automate Help X Calibri 11 ~ A A ab Wrap Text General Paste B I U v A v A Merge & Center v $ % 90-00 Clipboard Font Alignment Number Comments Sh AutoSum Fill Conditional Format as Cell Formatting Table Styles Insert Delete Format Clear Sort & Find & Filter Select Sensitivity Add-ins Analyze Data Styles Cells Editing Sensitivity Add-ins Don't show again Save As... i POSSIBLE DATA LOSS Some features might be lost if you save this workbook in the comma-delimited (.csv) format. To preserve these features, save it in an Excel file format. A1 fx loan_id A B C D E F H K M N 1 loan_id no_of_dependents education self_employed income_annum loan_amount loan_term cibil_score residential_assets_value commercial_assets_value luxury_assets_value bank_asset_value loan_status 2 3 1 2 2 Graduate No 0 Not Graduate Yes 9600000 4100000 29900000 12200000 12 778 2400000 17600000 8 417 2700000 2200000 22700000 8800000 8000000 Approved 3300000 Rejected 4 3 3 Graduate No 9100000 29700000 20 506 7100000 4500000 33300000 12800000 Rejected 5 4 3 Graduate No 8200000 30700000 8 467 18200000 3300000 23300000 7900000 Rejected 6 7 8 9 10 11 12 1345678222222222 19 20 5678902222222222 5 Not Graduate Yes 9800000 24200000 20 382 12400000 8200000 29400000 5000000 Rejected 0 Graduate Yes 4800000 13500000 10 319 6800000 8300000 13700000 5100000 Rejected 5 Graduate No 8700000 33000000 4 678 22500000 14800000 29200000 4300000 Approved 2 Graduate Yes 5700000 15000000 20 382 13200000 5700000 11800000 6000000 Rejected 0 Graduate Yes 800000 2200000 20 782 1300000 800000 2800000 600000 Approved 10 5 Not Graduate No 1100000 4300000 10 388 3200000 1400000 11 4 Graduate Yes 2900000 11200000 2 547 8100000 4700000 12 2 Not Graduate Yes 6700000 22700000 18 538 15300000 13 3 Not Graduate Yes 5000000 11600000 16 311 6400000 5800000 9600000 14 2 Graduate Yes 9100000 31500000 14 679 10800000 16600000 3300000 9500000 20400000 14600000 20900000 1600000 Rejected 3100000 Approved 6400000 Rejected 4300000 Rejected 5000000 Approved 15 1 Not Graduate No 1900000 7400000 6 469 1900000 1200000 5900000 1900000 Rejected 16 5 Not Graduate No 4700000 10700000 10 794 5700000 17 2 Graduate Yes 500000 1600000 4 663 1300000 18 4 Not Graduate Yes 2900000 9400000 14. 780 2900000 3900000 100000 2800000 16400000 4400000 Approved 1300000 700000 Approved 19 2 Graduate No 2700000 10300000 10 736 1000000 0 5 Graduate No 6300000 14600000 12 652 10300000 3500000 21 2 Graduate No 5000000 19400000 12 315 9500000 1600000 4 Graduate No 5800000 14000000 16 530 3800000 11300000 6700000 6200000 23500000 18000000 22200000 4300000 Approved 3300000 Approved 5900000 Approved 6100000 Rejected 5400000 Rejected 23 4 Graduate Yes 6500000 25700000 18 311 13100000 1700000 19500000 8500000 Rejected 24 0 Not Graduate Yes 500000 1400000 2 551 900000 0 Not Graduate No 4900000 9800000 16 324 3800000 5 Not Graduate No 3100000 9500000 20 514 7900000 600000 8700000 3100000 1100000 300000 Approved 10000000 3300000 Rejected 6600000 2600000 Rejected