Data cleaning and preprocessing are crucial steps in the data analysis process. They involve identifying and addressing issues and inconsistencies in the raw data to ensure its quality, accuracy, and suitability for further analysis. Proper data cleaning and preprocessing enhance the reliability and effectiveness of data...

Exploratory Data Analysis (EDA) is a critical step in the data analysis process that involves visually and quantitatively exploring the data to gain an initial understanding of its characteristics, patterns, and relationships. EDA helps data analysts and data scientists to identify potential issues, discover insights,...

Data analysis problems can vary widely depending on the nature of the data, the objectives of the analysis, and the specific domain or industry. However, some common data analysis problems that arise in different fields include:Exploratory Data Analysis (EDA): Understanding the structure, distribution, and basic characteristics...

Softmax is an activation function used primarily in the output layer of multi-class classification neural networks. It takes a vector of raw, unnormalized scores and converts them into a probability distribution over the different classes. The output of the softmax function can be interpreted as the likelihood or probability...

Hyperbolic Tangent, commonly referred to as tanh, is an activation function frequently used in artificial neural networks. It is an extension of the sigmoid function but maps the input to a range between -1 and 1, making it zero-centered and capable of handling both positive and negative inputs. The tanh function exhibits...

Leaky Rectified Linear Unit (Leaky ReLU) is a variant of the ReLU activation function that addresses the "dying ReLU" problem. The "dying ReLU" problem occurs when ReLU neurons become inactive for certain inputs during training, resulting in those neurons always outputting zero and not learning anything further.Leaky...

Rectified Linear Unit (ReLU) is a popular activation function used in artificial neural networks, especially in deep learning architectures. It addresses some of the limitations of older activation functions like the sigmoid and tanh functions. ReLU introduces non-linearity to the network and allows it to efficiently...