Dhruv Pal’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 #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

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

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. #prodigyinfotech

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

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

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." #prodigyInfotech

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

Visual exploration is a crucial first step in data analysis. By transforming raw data into meaningful charts and graphs, you can uncover hidden patterns, identify outliers, and gain a deeper understanding of your dataset. Remember, a picture is often worth a thousand words. #dataanalysis #datascience #visualization #bestpractices