Data Science Project

Date: 10-11-2022 Company name: CRED Role: ML Engineer Topic: kmeans, dbscan, linear regression,  boxplot 1. Is Feature Scaling required for the K means Algorithm? Yes, K-Means typically needs to have some form of normalization done on the datasets to work properly since it is sensitive to both the mean and variance of the datasets.For performing feature scaling, generally, StandardScaler is recommended, but depending on the specific use cases, other techniques might be more suitable as well. 2. What are the assumptions of linear regression? · Linearity of independent and dependent variable · Errors should be normally distributed with mean of zero · Errors have equal variance · Errors are independent 3. What is the use of a box plot? Box plots help visualize the distribution of quantitative values in a field. They are also valuable for comparisons across different categorical variables or identifying outliers, if either of those exist in a dataset. Box plots typically detail the minimum value, 25th percentile (Q1), median (50th percentile), 75th percentile (Q3) and the maximum value in a visual manner. 4. How does the epsilon value affect the DBSCAN Clustering Algorithm? The DBSCAN Algorithm is sensitive to the choice of epsilon. When we have clusters with varying densities, then two cases arise i.e., If epsilon is too small: In such cases, we define the sparser clusters as noise i.e, result in the elimination of sparse clusters as outliers. If epsilon is too large: In such cases, the denser clusters may be merged together, which gives the incorrect clusters.

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Date: 07-11-2022 Company name: Brookfield Role: ML Engineer Topic: Gini index, gradient descent, logistic regression, dbscan 1. Explain Gradient Descent algorithm. Ans. Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model. Parameters refer to coefficients in Linear Regression and weights in neural networks. 2. What is logistic regression used for classification instead of linear regression? Ans. Using linear Regression , all predictions >= 0.5 can be considered as 1 and rest all < 0.5 can be considered as 0. But then the question arises why classification can’t be performed using it? Suppose we are classifying a mail as spam or not spam and our output is y, it can be 0(spam) or 1(not spam). In case of Linear Regression, hθ(x) can be > 1 or < 0. Although our prediction should be in between 0 and 1, the model will predict value out of the range i.e. maybe > 1 or < 0. So, that’s why for a Classification task, Logistic/Sigmoid Regression plays its role. 3. What is the Gini Index? Ans. Gini Index is a score that evaluates how accurate a split is among the classified groups. Gini index evaluates a score in the range between 0 and 1, where 0 is when all observations belong to one class, and 1 is a random distribution of the elements within classes. In this case, we want to have a Gini index score as low as possible. Gini Index is the evaluation metrics we shall use to evaluate our Decision Tree Model. 4. Why is DBSCAN used over K means and other clustering methods? Ans. Partitioning methods (K-means, PAM clustering) and hierarchical clustering work for finding spherical-shaped clusters or convex clusters. In other words, they are suitable only for compact and well-separated clusters. Moreover, they are also severely affected by the presence of noise and outliers in the data. Real life data may contain irregularities, like: Clusters can be of arbitrary shape like non convex clusters Data may contain noise. Given such data, k-means algorithm has difficulties in identifying these clusters with arbitrary shapes.

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