Nikhil P S’ Post

🚢 Thrilled to announce the completion of my first task in data science internship by Encryptix! I used the iconic Titanic dataset to develop a predictive model that determines whether a passenger survived the tragic voyage. 🌊✨ 📊 Key aspects of the project included: Data Preprocessing: 🧹 Cleaning and transforming raw data, handling missing values, and encoding categorical variables. Exploratory Data Analysis (EDA): 🔍 Uncovering insights and visualizing patterns related to passenger demographics and survival rates. Feature Engineering: 🛠️ Creating meaningful features from existing data, such as family size and title extraction. Model Building: 🧠 Implementing various machine learning algorithms like Logistic Regression, Decision Trees, and Random Forests to identify the best predictor of survival. Model Evaluation: 📈 Assessing model performance using metrics such as accuracy, precision, recall, and ROC-AUC scores to ensure robust predictions. This project was an incredible journey, reinforcing my skills in data analysis, feature engineering, and machine learning model development. 🌟 Excited to apply these skills to real-world problems and continue my growth in the field of data science! 🚀 #DataScience #MachineLearning #Encrytix #PredictiveModeling #DataAnalysis #FeatureEngineering #CareerGrowth #ExploratoryDataAnalysis

Hello Connections I have completed the first task as a Machine Learning Intern at #prodigyinfotech . This task involved creation of a linear regression model to predict the price of house. In this task i performed data cleaning, missing value imputation,f eature selection on the data to train the model. #prodigyinfotech #machinelearning #datascience #dataanalytics

Exciting update from my internship at Encryptix! I have successfully completed Task 2 of exploring the Movie Rating Prediction dataset. I utilized various machine learning algorithms such as Random Forest, Decision Tree, Gradient Boosting, Linear Regression, and k-Nearest Neighbors (kNN). The model predicts movie ratings based on factors like director and the main actors (actor 1, actor 2, actor 3). The Random Forest model achieved high accuracy, showcasing the effectiveness of our approach. 🙌 Gratitude to Encryptix for the invaluable learning journey and mentorship. Thrilled to contribute to impactful projects in data science!  Check out the full project on my GitHub: https://lnkd.in/duwpUcHu #DataScience #MovieRatingPrediction #Encrptix. Encryptix

Excited to share my latest project as an intern at Encryptix! I developed a machine learning model that predicts fraud detection and analysis on the Content recommendation dataset with remarkable accuracy. *Key Highlights: Built and fine-tuned a predictive model for Content recommendation dataset. Developed a suite of visualizations for in-depth data analysis Leveraged advanced techniques to enhance prediction accuracy. #datascience #machinelearning #skilldevelopment #analysis

I'm excited to share my recent project on predicting Titanic survivors using machine learning techniques! Task1 completed as a datascience intern CodSoft. Project Overview: 📊 Data Exploration and Preparation: Data Handling: Loaded the Titanic dataset, filled missing values, and dropped the 'Cabin' column. Encoding: Transformed 'Sex' and 'Embarked' columns using LabelEncoder. 🛠️ Feature Engineering: Selected Features: 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked'. Standardization: Scaled features using StandardScaler. 🔍 Model Training: Logistic Regression & Random Forest: Trained both models and evaluated them on the test set. 📈 Performance Evaluation: Metrics: Assessed accuracy, confusion matrix, and classification reports for both models. Cross-Validation: Performed 5-fold cross-validation for the Random Forest model. 🔍 Insights and Future Work: Results: Both models showed promising results. Future improvements include advanced feature engineering and hyperparameter tuning. 🔗 GitHub Repository: https://lnkd.in/ex69pttQ #MachineLearning #DataScience #TitanicDataset #PredictiveAnalytics #DataScienceIntern#codsoft#titanicSurvivalPrediction

Internship project video explanation! Task 1- Iris Flower Classification by data science In this video, I dive into the classic Iris dataset, exploring how data science techniques can be applied to classify iris flower species with precision. By leveraging machine learning algorithms, particularly logistic regression, we can build a robust model that accurately predicts the species based on sepal and petal measurements. Github repository link: https://lnkd.in/gkSFz3qk #OasisInfobyte

I have completed my task five "Email spam Detection" of Data Science Internship with Oasis Infobyte Sales Prediction! Leveraging machine learning techniques, I've developed a model that predicts sales based on various features. #MachineLearning #salesprediction #OasisInfobyte

Excited to share my first project as a Data Science Intern at CodSoft: Titanic Survival Prediction! In order to analyze and forecast the Titanic passenger survival rate, I delved into the exciting field of predictive modeling for this project. I looked at a variety of factors, including age, class, and embarkation point, using machine learning algorithms to create a strong model. Digging into the data of one of the most famous historical events and using data science approaches to extract insights has been an amazing experience. Looking forward to the lessons to come and the upcoming projects! #DataScience #TitanicSurvivalPrediction #MachineLearning #CodSoftProjects #InternshipJourney GitHub link - https://lnkd.in/dCmv46N7

🌼 Excited to share my latest project on iris flower classification using data science techniques! 🌸 In this video, I demonstrate the step-by-step process of classifying iris flowers into three different classes: setosa, versicolor, and virginica. 📊 Project Highlights: Dataset: Downloaded the iris dataset from Kaggle and imported necessary libraries. Data Visualization: Visualized the dataset using seaborn to understand its characteristics. Data Preprocessing: Checked for missing values and split the data into training and test sets. Modeling: Used a decision tree classifier to predict the flower classes with uniform and accurate results. 🔍 This project showcases my hands-on experience in data analysis and machine learning techniques. Encryptix #internship #encryptix #datascience #machinelearning

I have completed my task five "Iris Flower Classification" of Data Science Internship with Oasis Infobyte Sales Prediction! Leveraging machine learning techniques, I've developed a model that predicts sales based on various features. #MachineLearning #salesprediction #OasisInfobyte