Level 1 Task Reports

11 / 6 / 2022

Task 1 Report # Linear and Logistic Regression - HelloWorld for AIML ## Overview Regression analysis is a statistical methodology that allows us to determine the strength and relationship of two variables. Regression is not limited to two variables, we could have 2 or more variables showing a relationship. The results from the regression help in predicting an unknown value depending on the relationship with the predicting variables. For example, someone’s height and weight usually have a relationship. Generally, taller people tend to weigh more. We could use regression analysis to help predict the weight of an individual, given their height. When there is a single input variable, the regression is referred to as Simple Linear Regression. We use the single variable (independent) to model a linear relationship with the target variable (dependent). We do this by fitting a model to describe the relationship. If there is more than predicting variable, the regression is referred to as Multiple Linear Regression. Logistic regression is a classification algorithm used to assign observations to a discrete set of classes. Some of the examples of classification problems are Email spam or not spam, Online transactions Fraud or not Fraud, Tumor Malignant or Benign. Logistic regression transforms its output using the logistic sigmoid function to return a probability value. What are the types of logistic regression? 1.Binary 2.Multinomial 3.Ordinal The complete task report for the given task is available in the link below Task 1 report #Task 2 Report #Matplotlib and Visualizing Data ## Overview Data visualization involves graphically plotting data and is an effective way to communicate inferences from data. Using data visualization, we can get a visual summary of our data. With pictures, maps and graphs, the human mind has an easier time processing and understanding any given data. Data visualization plays a significant role in the representation of both small and large data sets, but it is especially useful when we have large data sets, in which it is impossible to see all of our data, let alone process and understand it manually. The complete task report for the given task is available in the link below Task 2 report #Task 3 Report ##Metrics and Performance Evaluation ##Overview Performance metrics are a part of every machine learning pipeline. They tell you if you’re making progress. Every machine learning task can be broken down to either Regression or Classification, just like the performance metrics Metrics are used to monitor and measure the performance of a model (during training and testing), and don’t need to be differentiable. Evaluation metrics explain the performance of a model. An important aspect of evaluation metrics is their capability to discriminate among model results. The complete task report for the given task is available in the link below Task 3 report #Task 4 Report ##Linear and Logistic Regression - Coding the model from SCRATCH ##Overview Linear regression and logistic regression model can be implemented from scratch using various functions which includes mathematical implementation. The complete task report for the given task is available in the link below Task 4 report #Task 5 Report #K- Nearest Neighbor Algorithm ##Overview KNN also called K- nearest neighbour is a supervised machine learning algorithm that can be used for classification and regression problems. K nearest neighbour is one of the simplest algorithms to learn. K nearest neighbour is non-parametric . It does not make any assumptions for underlying data assumptions. K nearest neighbour is also termed as a lazy algorithm as it does not learn during the training phase rather it stores the data points but learns during the testing phase. It is a distance-based algorithm. The complete task report for the given task is available in the link below Task 5 report ###The source code for the the 5 tasks are available in the links provided below Task 1-Linear regression using scikit-learn Task 1-Logistic regression using scikit-learn Task 2-Matplotlib and Visualizing Data Task 3-Metrics Task 4-Linear Regression Algorithm from scratch Task 4-Logistic Regression Algorithm from scratch Task 5-KNN algorithm using sklearn Task 5-KNN algorithm from scratch