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abhisheks008 · Jan 10, 2024 · defef87 · defef87
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diff --git a/Bearing Classification/Dataset/Faulty-bearing.csv b/Bearing Classification/Dataset/Faulty-bearing.csv
diff --git a/Bearing Classification/Dataset/Healthy-bearing.csv b/Bearing Classification/Dataset/Healthy-bearing.csv
diff --git a/Bearing Classification/Images/EDA1.png b/Bearing Classification/Images/EDA1.png
diff --git a/Bearing Classification/Images/EDA2.png b/Bearing Classification/Images/EDA2.png
diff --git a/Bearing Classification/Images/EDA3.png b/Bearing Classification/Images/EDA3.png
diff --git a/Bearing Classification/Images/Input_Dataset.png b/Bearing Classification/Images/Input_Dataset.png
diff --git a/Bearing Classification/Images/Metrics.png b/Bearing Classification/Images/Metrics.png
diff --git a/Bearing Classification/Model/bearing_classification.ipynb b/Bearing Classification/Model/bearing_classification.ipynb
diff --git a/Bearing Classification/README.md b/Bearing Classification/README.md
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+# Bearing Classification using ML 
+
+## PROJECT TITLE
+
+Bearing Classification
+
+## GOAL
+
+To identify faulty and healthy bearing. 
+
+## DATASET
+
+The link for the dataset used in this project:  https://www.kaggle.com/datasets/zlemglsmklkaya/healthy-vs-faulty-bearings/data?select=Healthy-bearing.csv
+
+## EDA:
+![Alt text](Images/Input_Dataset.png)
+![Alt text](Images/EDA1.png)
+Shape: (1998,2)
+
+## DESCRIPTION
+
+This project aims to identify the faulty and helthy bearings.
+
+## WHAT I HAD DONE
+
+1. Data collection: From the link of the dataset given above. 
+2. Data preprocessing: Preprocessed the data to create valid features.
+3. Model selection: XGBC,Random Forest,Logestic Regression,Gaussian Bayes,AdaBoost Classifier.
+4. Comparative analysis: Compared the accuracy score of all the models.
+
+
+## MODELS SUMMARY
+
+- XGBC
+- Logistic Regression
+- Adaboost Classifier
+- Random Forest Classifier
+- Gaussian Bayes
+
+## LIBRARIES NEEDED
+
+The following libraries are required to run this project:
+
+- matplotlib
+- numpy
+- pandas
+- sklearn
+
+## EVALUATION METRICS
+
+The evaluation metrics I used to assess the models:
+
+- Accuracy 
+
+It is shown using Confusion Matrix in the Images folder
+
+## RESULTS
+Results on Val dataset:
+XGBC: 77.33%
+Random Forest: 77.67%
+Adaboost: 75%
+Logistic Regression: 74%
+Gaussian Bayes: 73.33%
+DTC:77.33%
+![Alt text](Images/Metrics.png)
+
+## CONCLUSION
+Based on results we can draw following conclusions:
+
+1.The Random Forest worked the best
diff --git a/BellaBeat - Capstone Project by Google/Dataset/.gitignore b/BellaBeat - Capstone Project by Google/Dataset/.gitignore
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+*.csv
diff --git a/BellaBeat - Capstone Project by Google/Dataset/README.md b/BellaBeat - Capstone Project by Google/Dataset/README.md
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+## Dataset
+
+Dataset can be found on the below link:
+
+[https://www.kaggle.com/datasets/dafertflores/google-capstone-project-bellabeat](https://www.kaggle.com/datasets/dafertflores/google-capstone-project-bellabeat)
diff --git a/...Beat - Capstone Project by Google/Images/calories_burned_by_day_of_the_week.png b/...Beat - Capstone Project by Google/Images/calories_burned_by_day_of_the_week.png
diff --git a/BellaBeat - Capstone Project by Google/Images/correlation_matrix.png b/BellaBeat - Capstone Project by Google/Images/correlation_matrix.png
diff --git a/BellaBeat - Capstone Project by Google/Images/linear_reg_1_x_test_vs_y_pred.png b/BellaBeat - Capstone Project by Google/Images/linear_reg_1_x_test_vs_y_pred.png
diff --git a/BellaBeat - Capstone Project by Google/Images/linear_reg_2_x_test_vs_y_pred.png b/BellaBeat - Capstone Project by Google/Images/linear_reg_2_x_test_vs_y_pred.png
diff --git a/... - Capstone Project by Google/Images/most_and_least_active_hours_of_the_day.png b/... - Capstone Project by Google/Images/most_and_least_active_hours_of_the_day.png
diff --git a/...eat - Capstone Project by Google/Images/sedentary_mins_vs_total_active_mins.png b/...eat - Capstone Project by Google/Images/sedentary_mins_vs_total_active_mins.png
diff --git a/BellaBeat - Capstone Project by Google/Images/total_active_mins_vs_total_steps.png b/BellaBeat - Capstone Project by Google/Images/total_active_mins_vs_total_steps.png
diff --git a/...apstone Project by Google/Images/total_steps_and_sedentary_mins_vs_calories.png b/...apstone Project by Google/Images/total_steps_and_sedentary_mins_vs_calories.png
diff --git a/...at - Capstone Project by Google/Images/total_steps_taken_by_day_of_the_week.png b/...at - Capstone Project by Google/Images/total_steps_taken_by_day_of_the_week.png
diff --git a/BellaBeat - Capstone Project by Google/Images/total_steps_vs_calories.png b/BellaBeat - Capstone Project by Google/Images/total_steps_vs_calories.png
diff --git a/...at - Capstone Project by Google/Images/total_time_asleep_by_day_of_the_week.png b/...at - Capstone Project by Google/Images/total_time_asleep_by_day_of_the_week.png