Sai-Likhith
Student at Gandhi Institute of Technology & Management (GITAM) University, Visakhapatnam
GITAMVisakhapatnam
Pinned Repositories
Decision-Tree-Classifier-using-Python
Applying Decision Tree Classifier model on open-source Diabetes Dataset
K-Means-Clustering
Applying K Means Clustering Model Machine Learning model on open-source Amazon.com Clustering Dataset
K-Nearest-Neighbors
Applying K Nearest Neighbors Machine Learning model on open-source Breast Cancer Detection Classification Master Dataset
Linear-Regression-Using-Python
Applying Linear Regreesion for Salary Dataset
Logistic-Regression-using-Python
Mobile-Price-Range-Prediction-A-Data-Science-Approach
Mobile Price Range Prediction: Use sales data to build a classification model for mobile phone price ranges. Features include battery power, camera, memory, and connectivity. Split data, apply logistic regression, KNN, SVM (linear and rbf), and evaluate using confusion matrices. Select the most accurate model.
Principal-Component-Analysis
Using Principal Component Analysis Dimensionality Reduction Technique in Machine Learning
Python-Guide
Comprehensive Guide on Python by Sai Likhith
Sentiment-Analysis-of-COVID-19-Tweets-A-Comparative-Analysis-of-Classification-Models
This aims to perform sentiment analysis on COVID-19 tweets using various classification models. We preprocess the data, convert words to vectors, and train models such as Naïve Bayes, SVM, and KNN. Finally, we compare their performance to determine the most accurate model for predicting sentiment in COVID-19 tweets.
Speech-to-Text-and-Speak-it-Out
A Python ML project that converts spoken language into text using speech recognition, and transforms text into spoken words using speech synthesis. Harness the power of machine learning to effortlessly transcribe and vocalize audio inputs. Enhance accessibility and communication in a streamlined, efficient manner.
Sai-Likhith's Repositories
Sai-Likhith/Mobile-Price-Range-Prediction-A-Data-Science-Approach
Mobile Price Range Prediction: Use sales data to build a classification model for mobile phone price ranges. Features include battery power, camera, memory, and connectivity. Split data, apply logistic regression, KNN, SVM (linear and rbf), and evaluate using confusion matrices. Select the most accurate model.
Sai-Likhith/Python-Guide
Comprehensive Guide on Python by Sai Likhith
Sai-Likhith/Sentiment-Analysis-of-COVID-19-Tweets-A-Comparative-Analysis-of-Classification-Models
This aims to perform sentiment analysis on COVID-19 tweets using various classification models. We preprocess the data, convert words to vectors, and train models such as Naïve Bayes, SVM, and KNN. Finally, we compare their performance to determine the most accurate model for predicting sentiment in COVID-19 tweets.
Sai-Likhith/Decision-Tree-Classifier-using-Python
Applying Decision Tree Classifier model on open-source Diabetes Dataset
Sai-Likhith/K-Means-Clustering
Applying K Means Clustering Model Machine Learning model on open-source Amazon.com Clustering Dataset
Sai-Likhith/K-Nearest-Neighbors
Applying K Nearest Neighbors Machine Learning model on open-source Breast Cancer Detection Classification Master Dataset
Sai-Likhith/Linear-Regression-Using-Python
Applying Linear Regreesion for Salary Dataset
Sai-Likhith/Logistic-Regression-using-Python
Sai-Likhith/Principal-Component-Analysis
Using Principal Component Analysis Dimensionality Reduction Technique in Machine Learning
Sai-Likhith/Speech-to-Text-and-Speak-it-Out
A Python ML project that converts spoken language into text using speech recognition, and transforms text into spoken words using speech synthesis. Harness the power of machine learning to effortlessly transcribe and vocalize audio inputs. Enhance accessibility and communication in a streamlined, efficient manner.
Sai-Likhith/Random-Forest-Classifier
Applying Random Forest Classifier Machine Learning model on open-source Breast Cancer Detection Classification Master Dataset
Sai-Likhith/SpamGuard-Naive-Bayes-Email-Classifier
SpamGuard is an efficient email classification system powered by Naive Bayes algorithm. It quickly analyzes the content of an email, evaluating its likelihood of being spam. With a simple input of the email body, SpamGuard accurately determines whether the email is spam or not, providing users with a reliable and effective tool for spam detection.
Sai-Likhith/Support-Vector-Machine-SVM-using-Python
Applying SVM ML model on open-source Diabetes Dataset
Sai-Likhith/Time-Series-Modelling
Performing Time Series Modelling on open-source Amazon.com Clustering Dataset