In the realm of data science, efficiency is key. Automating the process of Exploratory Data Analysis (EDA) in Python has become a game-changer, allowing professionals to focus more on deriving insights rather than getting lost in the preliminary stages of data exploration. By leveraging Python’s powerful libraries such as Pandas, NumPy, and Seaborn, data scientistsContinue reading “Exploring Efficiency: Automating Exploratory Data Analysis (EDA) in Python”
Tag Archives: EDA
Project Guide – Data Understanding & Data Sourcing (2/2) – Part 5
Precision in Understanding: EDA Techniques A crucial aspect of data understanding involves delving into Exploratory Data Analysis (EDA) which is an approach of analyzing datasets to summarize the containing main features. This entails both numerical and graphical summaries to paint a comprehensive picture of your dataset. Numerical Summaries: Decoding Data Through Numbers Depending on howContinue reading “Project Guide – Data Understanding & Data Sourcing (2/2) – Part 5”
Project Guide – Data Understanding & Data Sourcing (1/2) – Part 4
In our expedition through the realms of machine learning and data science, we’ve traversed the critical phase of “Problem Understanding,” laying the groundwork for the transformative journey that lies ahead. As we transition to the next phase, “Data Understanding and Data Sourcing,” let’s carry forward the insights gleaned from our problem understanding phase. The datasetsContinue reading “Project Guide – Data Understanding & Data Sourcing (1/2) – Part 4”
ML Zoomcamp 2023 – Machine Learning for Classification– Part 4
EDA – Exploratory Data Analysis The topics that we cover in this section are: Checking missing values The following snippet indicates that the dataset ‘df_full_train’ contains no missing values: Looking at the target variable (churn) First what we can check is the distribution of our target variable ‘churn’. How many customers are churning and howContinue reading “ML Zoomcamp 2023 – Machine Learning for Classification– Part 4”
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