Mahindra & Mahindra Data Science Engineer Interview Questions and Answers

Q1. What is the difference between linear and classification?
Ans. 
Linear regression is used for predicting continuous values, while classification is used for predicting discrete values.
• Linear regression is used when the output variable is continuous, such as predicting house prices based on features like size and location.

• Classification is used when the output variable is categorical, such as predicting whether an email is spam or not based on its content.

• Linear regression aims to f...read more
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Q2. Explain example of outlier
Ans. 
An outlier is a data point that differs significantly from other data points in a dataset.
• Outliers can skew statistical analyses and machine learning models.

• Examples of outliers include a person's weight being recorded as 1000 lbs, when the average weight is around 150 lbs.

• Outliers can be detected using statistical methods like Z-score or IQR.
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Q3. Explain k mean algorithm
Ans. 
K-means algorithm is a clustering technique that partitions data into k clusters based on similarity.
• Divides data points into k clusters based on centroids

• Iteratively assigns data points to the nearest centroid and updates centroids

• Continues until centroids no longer change significantly

• Example: Grouping customers based on purchasing behavior
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Q4. Explain the metrics for classification
Ans. 
Classification metrics are used to evaluate the performance of a classification model.
• Accuracy: measures the proportion of correctly classified instances out of total instances

• Precision: measures the proportion of true positive predictions out of all positive predictions

• Recall: measures the proportion of true positive predictions out of all actual positive instances

• F1 Score: harmonic mean of precision and recall, balan...read more
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Q1. Introduction your self
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Q2. What are the language you learned
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I have learned multiple programming languages including Python, R, SQL, and Java.
• Python

• R

• SQL

• Java
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It was campus placement. We were givien the topic "future of AI and ML in automobile industry". The time given was around 15mins. The explained the process in great detail and very clearly.

Q1. CV and basic of ML
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Q2. What if questions. For eg, if you want to launch a new EV in market then what will be your approach What if you are the CEO.. ETC
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Q1. Ethical questions
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Q2. Besic HR questions like why this company
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Q1. Questions related to projects mentioned in the resume
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Q2. What are the challenges that you have faced and how did you overcome those?
Ans. 
As a Data Scientist, I have faced challenges in data cleaning, model selection, and communication.
• Data cleaning: Dealing with missing values, outliers, and inconsistent data required careful preprocessing techniques.

• Model selection: Choosing the right algorithm and fine-tuning hyperparameters to achieve optimal performance.

• Communication: Effectively conveying complex technical concepts to non-technical stakeholders.

• Exa...read more
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Quantitative and reasoning

Q1. Standard questions. Nothing noteworthy
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Q1. 1. What is churn prediction.
Ans. 
Churn prediction is the process of identifying customers who are likely to stop using a service or product.
• Churn prediction uses historical data to build models that can forecast which customers are at risk of leaving.

• Common techniques for churn prediction include machine learning algorithms like logistic regression, decision trees, and neural networks.

• Businesses use churn prediction to proactively retain customers by ...read more
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Q2. 2. How Faster Rcnn better then Rcnn
Ans. 
Faster R-CNN is an improved version of R-CNN with a faster and more accurate object detection process.
• Faster R-CNN introduces Region Proposal Network (RPN) for generating region proposals, making the process faster.

• It combines the advantages of RPN and Fast R-CNN for improved speed and accuracy.

• Faster R-CNN achieves better performance in terms of speed and accuracy compared to R-CNN.

• It is widely used in computer vision...read more
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Q1. Previous experience, basic sql
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Q1. Can you work under pressure?
Ans. 
Yes, I can work under pressure.
• I have experience working on tight deadlines and delivering high-quality results.

• I am able to prioritize tasks and manage my time effectively.

• I remain calm and focused in stressful situations.

• I can adapt to changing priorities and handle multiple projects simultaneously.
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Q2. Where do you see yourself after 2/3/4/5 years?
Ans. 
In 2/3/4/5 years, I see myself as a senior data scientist leading a team, solving complex problems, and driving impactful insights.
• Leading a team of data scientists

• Solving complex problems using advanced analytics techniques

• Driving impactful insights for business decision-making

• Continuously learning and staying updated with the latest advancements in data science

• Contributing to the growth and success of the organizatio
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Q3. Why should we hire you?
Ans. 
I have a strong background in data science and a passion for problem-solving, making me a valuable asset to your team.
• I have a solid foundation in data science concepts and techniques.

• I am proficient in programming languages such as Python and R.

• I have experience working with various data analysis and visualization tools.

• I am a quick learner and adapt easily to new technologies and methodologies.

• I have excellent proble...read more
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