Madhuri Panchakshri’s Post

TASK 2- Perform data cleaning and exploratory data analysis (EDA) on a dataset of your choice, such as the Titanic dataset from Kaggle. Explore the relationships between variables and identify patterns and trends in the data. SkillCraft Technology hashtag #SkillCraftSkillCraft Technology hashtag #DataScience

TASK 2:- Perform data cleaning and exploratory data analysis (EDA) on a dataset of your choice, such as the Titanic dataset from Kaggle. Explore the relationships between variables and identify patterns and trends in the data. SkillCraft Technology hashtag #SkillCraftSkillCraft Technology #DataScience

TASK- 02 Perform data cleaning and exploratory data analysis (EDA) on a dataset of your choice, such as the Titanic dataset from Kaggle. Explore the relationships between variables and identify patterns and trends in the data. #ProdigyInfoTech #DataScienceIntern

TASK2 🤹♂️ Conduct data cleaning and exploratory data analysis (EDA) on a selected dataset, like the Titanic dataset available on Kaggle. Investigate inter-variable relationships, unveiling patterns and trends within the data. This comprehensive analysis enhances understanding and lays the foundation for informed decision-making in subsequent analytical processes. #DataScience #ByteUprise

project-2: Perform data cleaning and exploratory data analysis (EDA) on a dataset of your choice, such as the Titanic dataset from Kaggle. Explore the relationships between variables and identify patterns and trends in the data SkillCraft Technology

DS_PRODIGY_TASK_02 GitHub link: https://lnkd.in/gFBV9qmy I successfully completed a project on data cleaning and exploratory data analysis (EDA) using the Titanic dataset from Kaggle. This involved handling missing values, transforming data, and visualizing relationships between variables to identify key patterns and trends. The insights gained provide a comprehensive understanding of the factors influencing passenger survival. Prodigy InfoTech

Completed my second task at ProdigyInfoTech! Performed data cleaning and exploratory data analysis (EDA) on the Titanic dataset from Kaggle. Uncovered intriguing patterns and trends in the data. Loving the process of transforming raw data into valuable insights! #ProdigyInfoTech

🚀 Excited to be part of scorecard competition organized by Peaks2Tails (Karan Aggarwal) Initiated the Exploratory Data Analysis (EDA) and data preprocessing phase, thoroughly examining our dataset to discover its intricacies and revealing meaningful patterns. EDA is crucial for solid modeling, as it helps us grasp the data's core characteristics before progressing to essential tasks. Stay tuned for insights on the upcoming posts in this series, which will cover Weight of Evidence binning, Reject Inferencing, Logistic Regression, and Model Validation.💯 1/n #CreditRiskModeling #CreditRisk #PD #DataScience #MachineLearning

My second task is to Perform data cleaning and exploratory data analysis (EDA) on a dataset of your choice, such as the Titanic dataset from Kaggle. Explore the relationships between variables and identify patterns and trends in the data. Prodigy InfoTech #PRODIGYINFOTECH #PRODIGY

Sequencing different types of #machinelearning methods is so much fun. For example, I worked on a project once that was modeling time-to-event data and used 4 different methods to get to my needed classifier. Here's how it worked: Step 1: Used #RandomSurvivalForests to model right-censored time data and extract the top 5 features by importance Step 2: Built a #clustering algorithm to identify 3 clusters based on the top 5 features from the previous step Step 3: Modeled the right-censored time data with a #KaplanMeier curve to observe the differences by cluster from the previous step Step 4: Used #logisticregression to classify the "high risk" cluster as observed from the survival curve in the previous step using the top five features from the first step There's no one way to solve a problem in #datascience! #data #analytics #predictivemodeling