Exploratory Data Analysis (EDA) and Feature Engineering are two essential steps in data science projects that help in understanding the data, extracting valuable insights, and preparing the data for model building and analysis. Exploratory Data Analysis (EDA): EDA is the initial and crucial phase of any data science project. It involves exploring and summarizing the main characteristics of the dataset to gain insights into its structure, patterns, and relationships between variables. The main objectives of EDA are as follows: Data Cleaning: Identifying and handling missing or erroneous data points, dealing with outliers, and removing duplicates. Descriptive Statistics: Calculating basic statistical measures such as mean, median, standard deviation, and percentiles to understand the central tendencies and dispersion of the data. Data Visualization: Creating visual representations like histograms, scatter plots, box plots, and heatmaps to visualize the distribution and relationships between variables. Correlation Analysis: Assessing the correlation between different features to understand their interdependencies and potential multicollinearity. Hypothesis Testing: Conducting statistical tests to validate assumptions and make data-driven decisions. EDA helps data scientists to identify patterns, trends, and potential issues within the dataset. It provides a foundation for further analysis and model building. Feature Engineering: Feature engineering involves transforming the raw data into meaningful features that can be used as inputs for machine learning algorithms. The quality and relevance of features play a significant role in the performance of a predictive model. The key steps in feature engineering are as follows: Feature Selection: Choosing the most relevant features that have a significant impact on the target variable while disregarding irrelevant or redundant ones. This step helps in reducing dimensionality and enhancing model efficiency. Feature Transformation: Applying mathematical or statistical transformations to the features to make the data suitable for modeling. Common transformations include scaling, normalization, and log transformations. Handling Categorical Variables: Converting categorical variables into numerical representations using techniques like one-hot encoding or label encoding to make them usable by machine learning algorithms. Creating Interaction Features: Introducing new features based on interactions between existing features can help capture non-linear relationships. Handling Missing Data: Dealing with missing data by imputing or removing missing values, depending on the nature of the dataset. Feature Extraction: Generating new features from the existing data using domain knowledge or advanced techniques like principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE). Effective feature engineering can significantly improve the performance of machine learning models by providing them with more relevant and informative inputs, leading to more accurate predictions and better insights. In summary, Exploratory Data Analysis (EDA) helps in understanding the data, identifying patterns, and making data-driven decisions. Feature engineering transforms the data into useful features, enabling machine learning models to learn from the data and make predictions effectively. Together, these two steps are fundamental for successful data science projects.
1. Feature Engineering (Takes 30% of Project Time) a) EDA i) Analyze how many numerical features are present using histogram, pdf with seaborn, matplotlib. ii) Analyze how many categorical features are present. Is multiple categories present for each feature? iii) Missing Values (Visualize all these graphs) iv) Outliers - Boxplot v) Cleaning b) Handling the Missing Values i) Mean/Median/Mode c) Handling Imbalanced dataset d) Treating the Outliers e) Scaling down the data - Standardization, Normalization f) Converting the categorical features into numerical features 2. Feature Selection a) Correlation b) KNeighbors c) ChiSquare d) Genetic Algorithm e) Feature Importance - Extra Tree Classifiers 3. Model Creation 4. Hyperparameter Tuning 5. Model Deployment 6. Incremental Learning
Hi Krishna sir, I got new job on data science domain at Chennai product based company. Your videos lots help me before I was working different domain. Best Regards, Balaji
Guys I have doubt, can anyone help. For scaling data: we have numerical column and categorical column are encoded in to numerical. So scaling need to done only on numerical column or on encoded column as well.
What I actually need you know very well sir but how ??man ki baat jan lete ho ap antaryami ho mahagyani ho balki me to kahta hu ap purush he nahi MahaPurus ho🤩😍😍❤❤❤
Thanks Krish for the video I am about to start my first ever project as an intern and this helped me in an very deep way . Thank you 🙂 . If you give me any suggestions that would be very helpful for me .
Grt list of videos for EDA. In case we have more categorical variables and less numerical variables. Post EDA, should we work on Chaid algorithm. Please suggest. Thanks
@krish Naik, I am following your channel from the early days. I have a question, How to use information extracted from EDA? e.g by plotting a CDF graph, I can say that 70 % of people are below the age of 50. But the question is, where this information is used in the project?
Sir but, before doing EDA we can also split the data first, so that the test data can be completely isolated and don't have any idea about the training one. And then we can perform EDA on training data and further transform the test data. Is this a good practice? or do we perform EDA for complete data?
In theory you can create the training/test split at any point of the "pipeline". Generally you are sampling data points based on some distribution, or at random, and classifying those records as training/testing. That being said, you want the same transformations applied to the training and testing so you can apply one inverse function to revert these transformations. For example, if you are doing MinMax scaler, if you apply this after splitting then the inverse to undo the normalization will be different for each since the min/max for each dataset is different. So idealy you apply feature engineering on the dataset as a whole before splitting.
Sir I am doing MSc integrated in data science(BSC+MSc) in Goa, so in 5th semester they will teach us machine learning so should I do MLDL from ineuron ?? And can u suggest course which will be plus point for my career
If u are planning for job in AI or ML , then go for AppliedAI course.. if u are learning for your knowledge , u can consider Krish sir playlist or courses from Ineuron..
sir , for data columns which had more no. of zeros , we have to replace by mean,meadian, in numerical column. should we consider those zeros as missing values . for my data set belongs to timerseries which hads spends vs sales columns in different week level .i saw a column, spends in one channel is having too many zeros, what to do in this case?
