Machine-Learning-Project

To develop a machine learning model by using different ML Algorithm A machine learning project is a task that involves building, training, and evaluating a machine learning model to solve a specific problem. The process typically involves the following steps:

Problem formulation: Clearly define the problem you want to solve and identify the data you need to solve it.
Data collection: Gather and preprocess the data required for the project.
Exploratory data analysis (EDA): Perform a thorough analysis of the data to understand its properties and identify patterns and trends.
Feature engineering: Select and transform the relevant features that will be used to train the model.
Model selection: Choose an appropriate machine learning model based on the problem and data.
Training: Train the selected model using the training data.
Model evaluation: Evaluate the model's performance on the test data.
Hyperparameter tuning: Optimize the model's hyperparameters to improve its performance.
Deployment: Deploy the model in a production environment to make predictions on new data.

Some examples of machine learning projects include building a recommendation system for an e-commerce website, predicting customer churn for a telecommunications company, and detecting fraudulent credit card transactions.

Name		Name	Last commit message	Last commit date
Latest commit History 78 Commits
SimpleLinearRegressionSalary hike.ipynb		SimpleLinearRegressionSalary hike.ipynb
50_Startups.csv		50_Startups.csv
Ada-Boost Implementation.ipynb		Ada-Boost Implementation.ipynb
AdaBoost Algorithm.ipynb		AdaBoost Algorithm.ipynb
Advertising_dataset.csv		Advertising_dataset.csv
Algerian_forest_fires_dataset.csv		Algerian_forest_fires_dataset.csv
Batch Gradient Descent.ipynb		Batch Gradient Descent.ipynb
Boston.csv		Boston.csv
Build _model_predict_titanic_dataset.ipynb		Build _model_predict_titanic_dataset.ipynb
CKD.csv		CKD.csv
Cars.csv		Cars.csv
Carseats.csv		Carseats.csv
CatBoost Vs XGBoost Vs LightGBM.ipynb		CatBoost Vs XGBoost Vs LightGBM.ipynb
Column Transformer.ipynb		Column Transformer.ipynb
Credit Card Fraud Detection Project.ipynb		Credit Card Fraud Detection Project.ipynb
DBSCAN Anomaly Detection.ipynb		DBSCAN Anomaly Detection.ipynb
DBSCAN Clustering.ipynb		DBSCAN Clustering.ipynb
Decision Tree Regressor.ipynb		Decision Tree Regressor.ipynb
Decision_Tree_Classifier1.ipynb		Decision_Tree_Classifier1.ipynb
Decission_Tree_Regressor.ipynb		Decission_Tree_Regressor.ipynb
Decission_Tree_Salaries.ipynb		Decission_Tree_Salaries.ipynb
Decission_tree_Classifier.ipynb		Decission_tree_Classifier.ipynb
Diamond_Price_Prediction.csv		Diamond_Price_Prediction.csv
Diamond_Price_Prediction_Lasso_Ridge_Elasticnet.ipynb		Diamond_Price_Prediction_Lasso_Ridge_Elasticnet.ipynb
ElasticNet Regression.ipynb		ElasticNet Regression.ipynb
Gradient Boosting Algorithm.ipynb		Gradient Boosting Algorithm.ipynb
Gradient Descent - Optimization .ipynb		Gradient Descent - Optimization .ipynb
Gradient Descent.ipynb		Gradient Descent.ipynb
HR.csv		HR.csv
Health insurance_Gradient Boosting Algorithm .ipynb		Health insurance_Gradient Boosting Algorithm .ipynb
Hierarichal Clustering.ipynb		Hierarichal Clustering.ipynb
Imputing Numerical Data.ipynb		Imputing Numerical Data.ipynb
K-Means Clustering.ipynb		K-Means Clustering.ipynb
KNN_Classifier_Regression.ipynb		KNN_Classifier_Regression.ipynb
KNN_Implementation.ipynb		KNN_Implementation.ipynb
L1 and L2 Regularization.ipynb		L1 and L2 Regularization.ipynb
Lasso Regression - L1.ipynb		Lasso Regression - L1.ipynb
Linear Regression Algorithm.ipynb		Linear Regression Algorithm.ipynb
Linear Regression Multiple Variable.ipynb		Linear Regression Multiple Variable.ipynb
Linear Regression with multiple variables.ipynb		Linear Regression with multiple variables.ipynb
Linear_Regression_boston_housing_prices .ipynb		Linear_Regression_boston_housing_prices .ipynb
Local Outlier Factor(LOF).ipynb		Local Outlier Factor(LOF).ipynb
Logistic Regression - Softmax function.ipynb		Logistic Regression - Softmax function.ipynb
Logistic Regression Algortihm.ipynb		Logistic Regression Algortihm.ipynb
Logistic Regression Perceptron Sigmoid .ipynb		Logistic Regression Perceptron Sigmoid .ipynb
Logistic Regression_HR_Analytics.ipynb		Logistic Regression_HR_Analytics.ipynb
Logistic Regression_Insurance_data.ipynb		Logistic Regression_Insurance_data.ipynb
Logistic_Regression_Diabetes.pdf		Logistic_Regression_Diabetes.pdf
Logistics_Regression_classification.ipynb		Logistics_Regression_classification.ipynb
Machine Learning Example.ipynb		Machine Learning Example.ipynb
Machine Learning InterviewQnA.pdf		Machine Learning InterviewQnA.pdf
Machine Learning InterviewQue.pdf		Machine Learning InterviewQue.pdf
Machine Learning Statistics.ipynb		Machine Learning Statistics.ipynb
Mean Median Imputation.ipynb		Mean Median Imputation.ipynb
Mini Batch Gradient.ipynb		Mini Batch Gradient.ipynb
Model Building using ML Algorithm.ipynb		Model Building using ML Algorithm.ipynb
Model training student performance project.ipynb		Model training student performance project.ipynb
Multiple Linear Regression.ipynb		Multiple Linear Regression.ipynb
Multiple Linear Regression_50Startup.ipynb		Multiple Linear Regression_50Startup.ipynb
Multiple Linear regression_Toyota_Corolla.ipynb		Multiple Linear regression_Toyota_Corolla.ipynb
Naive_Bayes_titanic.ipynb		Naive_Bayes_titanic.ipynb
Naïve Bayes .ipynb		Naïve Bayes .ipynb
Naïve Bayes diabetes.ipynb		Naïve Bayes diabetes.ipynb
Normalization.ipynb		Normalization.ipynb
One_Hot_Encoding.ipynb		One_Hot_Encoding.ipynb
Prediction Supervised Machine Learning_Hours_Score.ipynb		Prediction Supervised Machine Learning_Hours_Score.ipynb
Prediction Using Decision Tree Algorithm.ipynb		Prediction Using Decision Tree Algorithm.ipynb
README.md		README.md
Random Forest Classifier.ipynb		Random Forest Classifier.ipynb
Random Forest.ipynb		Random Forest.ipynb
Random_Forest_Classifier.ipynb		Random_Forest_Classifier.ipynb
Random_Forest_Exercise.ipynb		Random_Forest_Exercise.ipynb
Random_forest_implementation.ipynb		Random_forest_implementation.ipynb
Regression Analysis Question.pdf		Regression Analysis Question.pdf
Regression Metrics.ipynb		Regression Metrics.ipynb
Regularized linear model - Ridge.ipynb		Regularized linear model - Ridge.ipynb
Ridge L2 Classifier.ipynb		Ridge L2 Classifier.ipynb
Ridge Regression - Coefficient Affected.ipynb		Ridge Regression - Coefficient Affected.ipynb
Ridge Regression - Regularized Linear Model.ipynb		Ridge Regression - Regularized Linear Model.ipynb
SWEETVIZ_REPORT.html		SWEETVIZ_REPORT.html
Salary_Data.csv		Salary_Data.csv
Scikit Learn Pipeline titanic csv.ipynb		Scikit Learn Pipeline titanic csv.ipynb
Simple Linear Regression .ipynb		Simple Linear Regression .ipynb
Simple Linear Regression Placement dataset.ipynb		Simple Linear Regression Placement dataset.ipynb
Simple_Linear_Regression_Assignment.ipynb		Simple_Linear_Regression_Assignment.ipynb
Simple_Linear_Regression_HW_Dataset.ipynb		Simple_Linear_Regression_HW_Dataset.ipynb
Social_Network_Ads.csv		Social_Network_Ads.csv
Standardization.ipynb		Standardization.ipynb
Stochastic gradient descent.ipynb		Stochastic gradient descent.ipynb
StudentsPerformance.csv		StudentsPerformance.csv
Support Vector Classifier.ipynb		Support Vector Classifier.ipynb
Support Vector Machine.ipynb		Support Vector Machine.ipynb
Support _Vector Classifier.ipynb		Support _Vector Classifier.ipynb
SweetVIZ Report.jpg		SweetVIZ Report.jpg
ToyotaCorolla1.csv		ToyotaCorolla1.csv
Use Case Analysis.ipynb		Use Case Analysis.ipynb
Weight_height.csv		Weight_height.csv
breast-cancer.csv		breast-cancer.csv
cars1.csv		cars1.csv
census_income.csv		census_income.csv

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

Machine-Learning-Project

About

Releases

Packages

Languages

sunilzambre/Machine-Learning-Project

Folders and files

Latest commit

History

Repository files navigation

Machine-Learning-Project

About

Resources

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages