Lakshmi I’s Post

Excited to share my completion of task-3 in my internship, focusing on Wine Quality Prediction! 🍷📊 Dataset Used for the project: 🔗 https://lnkd.in/g-xBYBGX Main Challenges: -Data Visualization: Utilized Seaborn and Matplotlib to visualize patterns and insights from the dataset. Also Obtained relationship between chemical attributes such as density and acidity, considering crucial predictors for wine quality. -Data Analysis: Plotted diverse graphs such as histplot and scatterplot , enhancing data visualization between different features in the dataset. -Data Preprocessing: Executed several operations on the dataset, including removing missing values, dropping columns, and converting certain columns into target columns. -ML Models: Employed a diverse set of models including Linear regression, Random Forest regression and gradient boosting for predicting wine quality. Achieved MSE score of 0.3 and R2 error as 0.40 🚀 Check out the project here: https://lnkd.in/gAUN9-QN #oasisinfobyte #DataAnalytics #DataScience #MachineLearning #WineQualityPrediction 🍇🔍

Hey all ✨ This is the last task of my Data Analytics Virtual internship program at Oasis Infobyte ✨ Task:-Red Wine Quality prediction IDE:-Jupyter Notebook The main aim of this project is to predict the quality of red wine whether it is a good or bad quality wine With the help of Logistic Regression Had predicted the wine quality Key steps:- 1)Data Cleaning:- Removed the outliers data 2)EDA:- Analyzed the data graphically through various plots 3) Logistic Regression:-Build the model and predicted the quality of wine You can check out my code on my GitHub Repository https://lnkd.in/gA9cXvn6 #oasisinfobyte #oasisinfobytefamily #DataAnalytics #Virtualinternshipprogram

Hello everyone, I am glad to share with you a project I carried out during my internship with Quantum Analytics NG PROJECT TOPIC: RESTAURANT RATINGS DATA SOURCE: QUANTUM ANALYTICS NG DATA VISUALIZATION TOOL: Powerbi KPIs/INSIGHTS DERIVED; NUMBER OF RESTAURANTS NUMBER OF STATES NUMBER OF CUISINES OVERALL RATING BY RESTAURANT OVERALL RATING BY STATES For more insights from the dashboard, kindly watch the clip attached. I would greatly appreciate your feedback and comments. Special thanks to my facilitators: Jonathan Osagie, Godswill Ojumeaka, #dataanalytics #textanalysis #descriptiveanalysis #predictiveanalysis #powerbidashboard #powerquery #datavisualization #commentbelow #interactivedashboards #dataenthusiast #datajourney #interactivedashboards #dataenthusiast #datajourney

I am passionate about data analytics and currently immersed in an internship at Oasis Infobyte, where I am dedicated to the intricacies of Wine Quality Prediction. You can explore the methodologies and outcomes on my GitHub repository: https://lnkd.in/gTgCmf5S #dataanalytics #datascience

🎉 Excited to announce the completion of my latest Power BI Dashboard project, "Red Wine Quality Analysis," as part of my internship at CognoRise InfoTech! 🍷 This dynamic dashboard delves into the intricacies of red wine quality, offering valuable insights into various attributes that contribute to its overall quality. 📊 Key features of the dashboard include: 1️⃣ Gauge visualization showcasing the Average Alcoholic Level and Quality of Red Wine, providing a quick snapshot of these crucial metrics. 2️⃣ Distribution analysis by attribute, allowing for a deeper understanding of how each attribute contributes to the overall quality of red wine. 3️⃣ Visualization of the relationship between Average Alcohol Level and Wine Quality, highlighting any trends or correlations. 4️⃣ Analysis of Average Total Sulphur dioxide by pH, offering insights into the impact of pH levels on wine quality. 5️⃣ Trendline graph depicting the relationship between Average Fixed Acidity and Density, aiding in understanding their interplay. 6️⃣ Treemap visualization providing an overview of all average attributes, facilitating comparison and analysis. 7️⃣ Exploration of how wine quality changes with variations in each input variable, offering actionable insights for wine producers and enthusiasts alike. 👨💼 Proud to have developed this insightful tool, leveraging data visualization techniques to unravel the mysteries of red wine quality. Grateful for the guidance and support received throughout my internship journey at CognoRise InfoTech. 🌟 Stay tuned for more exciting projects and insights! #PowerBI #DataVisualization #WineAnalysis #CognoRiseInfotech

I'm thrilled to share that I've recently completed tasks from Level 2 of my Data Science Internship at Cognifyz Technologies ! 💻📈 Here's a snapshot of the impactful tasks I've accomplished: Task 1: Exploring Restaurant Features - Uncovered the Restaurant Landscape: Determined the percentage of restaurants offering table booking and online delivery. - Rating Insights: Conducted a comparative analysis of average ratings for restaurants with and without table booking. - Pricing Strategies: Analyzed the availability of online delivery across different restaurant price ranges. Task 2: Delving into Price Range Analysis - The Price Point Pioneers: Identified the most common price range among all restaurants. - A Taste of Numbers: Calculated the average rating for each price range, offering valuable insights into the dining experience. - Color of Excellence: Pinpointed the color that symbolizes the highest average rating among diverse price ranges. Task 3: Crafting Enhanced Features - Elevating Data: Enhanced the dataset by extracting additional features, such as restaurant name and address lengths. - Categorical Transformation: Created new features, "Has Table Booking" and "Has Online Delivery," by encoding categorical variables.

Excited to announce completion of my traineeship in Data Analytics at MedTourEasy ! 🎉 Thrilled to delve into my project on "Analyzing the age difference in death between right-handers and left-handers". Let's unlock the secrets hidden in the data! 💻📊 #DataAnalytics #Research #MTETraineeship

Thrilled to announce that I’ve completed Level 1 of my Data Analytics internship at Cognifyz Technologies! 🚀 Here’s what I worked on and learned: 1️⃣ Top Cuisines – Analyzed datasets to identify popular cuisines and calculated their distribution across restaurants. 2️⃣ City Analysis – Improved my ability to visualize data by analyzing restaurant trends across cities. 3️⃣ Price Range Distribution – Enhanced my statistical skills by assessing restaurant price ranges and affordability. 4️⃣ Online Delivery – Explored the impact of online delivery services and their connection to business models. I developed strong skills in data cleaning, visualization, and deriving business insights and am excited to keep learning! Github repo link 🔗: https://lnkd.in/gqHRUqiP #DataAnalytics #CognifyzTechnology #SkillDevelopment #LearningJourney #BusinessInsights

🍇🍷Level 2 - Project 2 Success: Wine Quality Prediction! 🍇🍷 🎉 I’m excited to share that I’ve completed Project 2 from Level 2 of my data analytics internship at Oasis Infobyte! 🎉 In this project, I focused on predicting wine quality based on its chemical characteristics, involving: 🔬 Classifier Models: Using Random Forest, Stochastic Gradient Descent, and Support Vector Classifier (SVC). ⚗️ Chemical Qualities: Analyzing features like density and acidity as predictors for wine quality. 📊 Data Analysis: Employing Pandas for data manipulation and Numpy for array operations. 📈 Visualization: Utilizing Seaborn and Matplotlib to visualize patterns and insights. This project offered a real-world application of machine learning in viticulture, enhancing my skills in data analysis and model implementation. I’m eager to apply these insights to future projects! 🚀 #DataAnalytics #WineQualityPrediction #MachineLearning #DataScience #LearningJourney #Oasis

👉 I Successfully completed my first level of tasks in my internship about data science in Cognifyz Technologies , and learned about Data exploration, Data visualization, and Data analysis in restaurant data. 😍 🕐 TASK 1- Determine the top three most common cuisines in the dataset and Calculate the percentage of restaurants that serve each of the top cuisines. 🕑 TASK 2- Identify the city with the highest number of restaurants, Calculate the average rating for restaurants in each city. Determine the city with the highest average rating. 🕒 TASK 3 - Create a histogram or bar chart to visualize the distribution of price ranges among the restaurants. Calculate the percentage of restaurants in each price range category.  🕓 TASK 4- Determine the percentage of restaurants that offer online delivery. Compare the average ratings of restaurants with and without online delivery. check out the project on GitHub: https://lnkd.in/d87aUKWX #datascience #CognifyzCognifyz Technologies #intern #carrerdevelopment #steptodataworld