- Definition of Machine Learning
- Application of Machine Learning
- Activation Function
- Supervised Learning
- Unsupervised Learning
- Exploratory Data Analysis
- Simple Linear Regression
- Multiple Linear Regression
- Univariate Logistic Regression
- Multivariate Logistic Regression
- Introduction to Naive Bayes
- Naive Bayes for Categorical Data
- Naive Bayes for Text Classification
- Support Vector Machines
- Introduction to Decision Trees
- Introduction to Clustering
- K Mean Clustering
- K Mode Clustering
- DB Scan Clustering
- Pricipal Component Analysis
Machine Learning
Supervised Learning
Unsupervised Learning
What is Exploratory Data Analysis?
Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns, to spot anomalies, to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.”
Exploratory Data Analysis (EDA) is a critical step in the data analysis process. It involves visually and statistically exploring the data to gain insights, detect patterns, and identify potential issues or relationships between variables. EDA helps in understanding the data, formulating hypotheses, and guiding the next steps in data modeling and analysis.
- Data Collection: Gather the relevant data from various sources, such as databases, APIs, spreadsheets, or CSV files.
- Data Cleaning: Clean the data to handle missing values, outliers, and inconsistencies. This step ensures that the data is ready for analysis.
- Summary Statistics: Calculate basic summary statistics like mean, median, standard deviation, minimum, maximum, etc., for numerical variables. This provides an overall understanding of the data distribution
- Data Visualization: Create visualizations to explore the data's patterns and relationships. Common plots include histograms, bar charts, scatter plots, box plots, heatmaps, etc.
- Correlation Analysis: Examine the correlation between variables to identify potential dependencies and understand how they influence each other.
- Data Transformation: If necessary, perform data transformations such as normalization, log transformation, or scaling to make the data suitable for modeling.
- Feature Engineering: Create new features or derive meaningful features from existing ones that could potentially improve model performance.
- Outlier Detection: Identify and deal with outliers that might impact the accuracy of the analysis.
- Dimensionality Reduction: For high-dimensional datasets, use techniques like Principal Component Analysis (PCA) or t-distributed Stochastic Neighbor Embedding (t-SNE) to visualize and explore the data in lower dimensions.
- Time-Series Analysis (if applicable): For time-series data, analyze trends, seasonality, and other patterns over time.
- Hypothesis Testing: If you have specific questions or hypotheses, perform statistical tests to validate or reject them.
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