## Data scientist and Machine learning Question

**What is NumPy? How does it facilitate numerical computing in Python? Provide examples of commonly used NumPy functions**.

**What is pandas? How does it facilitate data manipulation and analysis in Python? Provide examples of commonly used pandas functions**.

**Explain the concept of data cleaning and preprocessing in the context of data science. Share some techniques and libraries in Python for handling missing values and outliers**.

**What is scikit-learn? How does it support machine learning tasks in Python? Provide examples of commonly used scikit-learn functions and algorithms**.

**What is the difference between supervised and unsupervised learning? Provide examples of each type and explain how Python can be used for implementation**.

**What are cross-validation techniques, and why are they important in machine learning? Explain how to perform cross-validation using Python libraries**.

**Describe your experience with feature selection and dimensionality reduction in machine learning. Share examples of techniques and Python libraries you have used**.

**Explain the concept of regularization in machine learning. How does it address overfitting, and what are commonly used regularization techniques in Python**?

**What is deep learning, and how does it differ from traditional machine learning? Describe your experience with deep learning frameworks in Python, such as TensorFlow or PyTorch**.

**How do you handle imbalanced datasets in machine learning? Share techniques and Python libraries you have used to address this issue**.

**Explain the concept of ensemble learning. What are commonly used ensemble methods, and how can they be implemented in Python**?

**What is natural language processing (NLP), and how does Python support NLP tasks? Describe your experience with NLP libraries, such as NLTK or spaCy**.

**Share an example of a data science or machine learning project you have worked on in Python. Discuss the problem statement, data preprocessing, modeling, and evaluation techniques used**.

**difference between structure and unstructured data in nlp**

**explain root cause analysis**

**what paramerter use in hyperparameter tuning in decision tree**

**explain dfference between decision tree and random forest**

**explain word to make in nlp**

**explain baggging and boosting**

**explain reinforcement learning**

## Machine Learning Interview Question

r2_score===================

radio,tv,newspaper

find r2 combinedly

find r2 seprately

standard deviatin== as small as then it goood for ml

quartile=25%,50%,75%

**standard deviation should be high or less for good machine learning**

```
Small
```

**For categorical not continouus mean or mode used not float**

```
mode
```

when mode take and means to fill nan

use dist plot to know normally distributed are not

find linnear relationship to know they are linearly dependent or not

**why use standadization scale**

```
to make equual bigger value to bring unit variance
transform the features of a dataset to have zero mean and unit variance, making them comparable and preventing features with larger scales
```

for features not for target y

trained and test explain with example

order of train and test

np.random.seed equal random_statepredict of chance of admission using scalar transform

save the model using pickle

how to read pickle data

regression_score== how much u understand train training and test data(r adjusted score)

predict answer regression_predict

plot the data to know how x test and ay ped by scatter and draw linearly regression

model evaluation

**IF u have to ignore outlier within linearly plotted**

```
mean square errormen squared error
```

if new set of data then my trained model cannot predict is called overfitting

to check overfitting== lasso,ridge model

lsso== zero importance to unrelavant features

ridge-- sime importance

learning rate for lasso and ridge

cross validate== 10 times read/validate

lasso== max-iteration

when lsso and ridge model is to be used

np.arrange when use

**logistic regression is used for what kind of data**

```
categorical==true/false pass/fail for classification
sigmoid== classify below some falue
```

evaluation of classifiction model

**confusion matrix is used for categorical/classification or continous data**

```
categorical/classification
logistic regression
```

Confusion matrix is used for only calssification model for lositic not for linear regression

corona actual and corona prediction

positive and negative

**Which is more dangerous type-1 error or type-II**

```
type 2 is more dangerous
```

precision recall accuracy

**Based on recall value how you determine model is trained good**

```
recall should be high
when minimizing false negatives is important
or when the cost of false negatives is high
```

**In which case precision should be considered for good trained model**

```
minimizing false positives is important or
when the cost of false positives is high
```

.

**Which Model Evaluation best to pick some part of both recall and precision and based on analysis determine prediction**

```
F1 score
```

**If threshold value change is all metric confusion matrix fl score, mean absolute error would be affected**

```
Yes
```

**Which Model Evaluation best to pick best trained model out of 5 or 6 model**

```
ROC curve
```

**To accurate prediction dataset should have more or less data**

```
More
```

**What is alpha**

```
error rate allowed
balancing between the model's complexity and its ability to fit the training data.
```

**More curved under roc in ml determine**

```
model's predictions are more accurate and reliable.
model has a higher discriminatory power and is able to distinguish between the positive and negative classes more effectively
```

**np.arrange(0, 10, 2)**

```
will create an array with values [0, 2, 4, 6, 8]
```

## Logistic regression method

**during data preprocessing == if u found any problem to find and treating outlier then again start data cleaning**

**Different way to replace the zero in dataset**

```
Mean/Median Imputation:
Mode Imputation:
Custom Value Imputation
Interpolation:
K-Nearest Neighbors (KNN) Imputation
Predictive Modeling
Zero-Filling for Sparsity
```

**replace the zero with meaning full information like mean**

**If there is no difference between Q1 and Q3 meaning**

```
data analysis is not good
we need data preprocessing those features
```

**why normally distribution of data is important**

```
beneficial in statistical modeling and inference.
very helpful to determine dependency between target and features
```

**plotted data contain left and right skew indicates**

```
more no of outlier present
```

**how to identify which outlier we have to keep and which outlier have to drop**

```
required domain knowledge
```

**which visualization technique is good for determine outliers**

```
boxplot
stripeplots
```

**what formulla is uset to determine outliers**

```
Quartile detection formulla
```

**what are the steps to remove outliers**

```
formulla outlier==
numpywherecondition ===
droping===
reset index
```

**If height of bar is empty or very less in normal distributed data**

```
sign of more outlier present
```

**how to solve multicolinearity problem**

```
VIF variance influence factor
```

**what value indicates variance inflation factor very less**

```
less than 5
```

**which model evalution is very helpful to determine true positive rate or false positive rate or tyoe 1 error or type 2 error**

```
confusion matrix
roc curve
```

**no difference between quartile of 25 % or 50 %if same then something wrong**

**draw the plots of all feature if it is distributed normally it is correct data but it has left skew then many outlier present**

**how to identify outlier then domain knowledge comes to very important role**

**boxplot helps to determine outliers **
**in boxplot= outlier identify by no of dots otherwise in normal distribution height of bar is very less means outlier
in boxplot we can identfy outlier are in leftskew or rightskew**

**situation arises where we need to keep outlier and where we need to remove outlier by domain knowledge like in bank balance we need to keep outlier**

**outlier can be determine by quartile detection formulla
get outlier using numpy where condition**

**after determining outlier drop that data where u outlier found**

**after deleting outlier reset index**

formulla outlier==

numpywherecondition ===

droping===

reset index

**still outlier after deleting we can keep that data not harmfull for analysis**

**keep those feature where we have relationship with labels by stripe plot== label and features**

**in stripe plot if x increases then y also increses**

after analysing x and y axis dependency we find that analysis

how to solve multicolinearity problem one feature dependent on another feature , salary dependent by age and exp that comes ur VIF variance influence factor

**finding VIF in each scaled colunm by using forloop range in python**

**if all vif are less than 5 very lowthen no multicolinearity relation ship**

**model prdiction by 0 and 1**

**roc curve determine true and false positive rate**

**determine how much area covered by aoc**

======================================================

## KNN/OVERFIITING/UNDERFITTING/BIAS TRADE OFF/VARIANCE

**knn is used for regression or classification**

```
both
```

**knn mostly used for regression or classification**

```
classification
```

**On what basis k value select in knn **

```
F1 score
cross validation
```

**what are the method to find distance between data set and k value**

```
Euclidean Distance
```

**knn is supervised or unsupervised learning**

**why knn is lazy learners**

```
lazy learners are instance-based algorithms
memorize the entire training dataset and
make predictions for new data points based on
similarity to the existing data instances.
```

**When knn should apply for which data set**

```
well-suited for smaller datasets with
well-defined local structures and
non-linear decision boundaries
```

**Most of the model used for preprocessing technique**

```
sk learn
```

**when data set imbalanced**

```
distribution of class labels is not equal,
more instances of one class compared to the other(s).
```

**Imbalanced dataset leads to overfitting or underfitting**

```
Underfittings
```

**Imbalanced dataset leads to bias or variance**

```
bias
```

**what is bais**

```
model is too simple to capture patterns and relationships
new, unseen data (test data)
```

**fluctuations in the training data and noise and random variations in the training data leads to ?**

```
variance and overfitting
```

**what is the multicolinearity porpose**

```
purpose to find best features
```

**A high-bias model tends to have low variance but high bias, leading**

```
underfitting
```

**A high-variance model tends to have low bias but high variance**

```
overfitting
```

**what are the method to balance between bais and variance or underfitting or over fitting**

```
bais trade off
```

**What are the method to remove bais and variance**

```
Cross-Validation:
Regularization:
k fold
leave on out of cross validation
k fold cross validation
```

**what is k fold cross validation**

```
the dataset is divided into k equal-sized folds
trained on k-1 folds and validated on the remaining one
repeated k times,
```

**what is leave on out of cross validation**

```
unbiased estimate of the model's performance
```

**what is Hyperparameter tuning**

```
avoid overfitting and underfitting
performs well on a wide range of datasets
computationally expensive and time-consuming,
```

**what is brute force methods**

all possible combinations of hyperparameter values within a predefined range to find the best set of hyperparameters

**Methods of hyperparameter tunning**

Grid Search

Randomized search

Bayesian Optimization

Genetic Algorithms

Gradient-based Optimization

**what is encoder**

that compresses and converts input data into a lower-dimensional representation like binary digit that ml understand

**what is ohe**

represents categorical variables as binary vectors

and encoder compact numerical representations of categorical variables

**what is simple imputter**

replaces missing values in a dataset with statistics

**by default simple imputter is use**

mean

**Difference between si and fillna**

replaces missing values in a dataset with statistics

fills missing values with specified values

**Difference between ordinal encoder and label encoder**

Ordinal encoder assigns integer values to ordinal categorical variables based on their order, while label encoder assigns unique integers to non-ordinal categorical variables in an arbitrary manner

**what is get_dummies**

get_dummies is a function used in pandas to convert categorical variables into dummy/indicator variables for machine learning

========================================================

**when binary encoder used**

large number of unique categories. or more than 5 category

**when ohe used**

number of unique categories is not very large less than 5 category to be used

**When KNNImputer to be used**

The KNNImputer replaces missing values in a dataset using the k-Nearest Neighbors approach

**When Iterative-Imputer to be used**

uses an iterative procedure to estimate the missing values based on other features

**In data scintist lifecycle when data selection to be used**

ignore nominal data like first name,sir name senior ,junior not used for analysis

**In data scintist lifecycle when data describe to be used**

see mean,null,empty,quartile ,standard deviation all thing observe

**In data scintist lifecycle when data data analysis to be used**

normal distribution,box plot ,skew,outlier,bais,variance,bar plot left and right skew,check relationship

**In data scintist lifecycle when data data transformation to be used**

ctegorical transformation,encoding technique

**In data scintist lifecycle selection ML algorithms**

wheather it is classification,regression prob for catrgorical

multi classs ==then decision tree

**In data scintist lifecycle data standard and normalization**

standard scalar not baised unitless

**When decision tree to be used**

regression and classification both best algorithm to deal complex dataset and for multicalss for classification most of time dt

**how to calculate most important feature to calculate considerd as root node**

using tree prunning or gini index

**when treeprunning to be used**

when u want to go go decision quickly then cut tree

**what is entropy**

how much information every feature have label which feture higest feature is comes under root node

**what is gini index**

how much every feature have impurity less impurity better feature

id3--

**What is CART**

classification and regressssion and both datatype

**by default we use gini index or entropy**

gini index

**When heatmap to be used**

heat map use to find multicolinearty relationship

**In metric score function, if train value is true**

feature is selected for training

**What visualization technique is selected to show multicolinearity**

heatmap and scatterplot

**IF testing accuracy is very less or huge difference between training accuracy and testing accuracy**

we need to improve testing accuracy

**when we stop to splitting decision tree**

when 1 leaf node rhat is pure node

**what is cv**

cv increse training time

**what are the method to tune the parameter in hyperparameter tunning**

entropy,gini index

**what are the ensamble approach**

bagging and boosting

**what are the boosting method**

adaboost,gradient boosting,xtreme boosting

**When we want to more than 2 or 3 model then we use**

ensamble approach

**why we use ensamble approach**

take decision based on 100 DT models not one DT

one DT take some feature random ly so model is not baised very less chance baised

**Advantage of bagging method**

very less risk,safe model but time,cost budget issue

**what is out of bag evalution**

some testing feature out of bag/dataset for all MODEl

**what is pasting**

pasting once the feature selected for first DT then it is not allowed for second DT,3rd dt ==bootstrap false and without replacement

**relation ship between features**

multicolinearity

**bagging and boosting performed operation**

`bagging==parallely`

boosting==sequencially

**By default random forest use**

decision tree

knn is supervised is both for regression(like linear) and classification(logistic)

mainly used for classification

k is random variable

pass ed test data

calculated distatince b/w test data and all training data setif i mentio k=3 find 3 nearest neighbour data points that is closest

how many green or red products covered has highest probability

deciding to challange what is value of k

how to calculate distance

=technique

by eucadian we calculate sum of distance

lazy learner== some of students are lazy learners

when exam announced then stared to prepare

whenever r ready to train test then predict waits for test data

knn is not best when too many data in dataset difficult to find som of distance

all the model for preprocessing steps use==sklearn

by which command we find data type of dataset

df.shape

classification== mcancer and b cancer

check dataset is imbalnced diference between m and b

bias= if imbalanced means then it predict only one thing m cancer type it is called bias

if model is bias then it is underfitting

multicolinaert== purpose to find best features

which feature contribute highest== by annova test

ml never understand object data type it inderstand 0 and 1

how to trained the mode on google cloud not in local

17 nlargest contribution feature in dataset using f1 score

choose how to select 17 (for k)see where to less differnce t

challenging part to decide k

feature selection is preprocessing technique

new way for feature selection to get value of k

train is false then it is for testing

test classification report support means 93 % belongs one category

how to increase accuracy score after classificatin by hyperparameter tuning,cross validation

by default k no is 5 kneighbour

ig good score in accuracy score may be overfitting to avoid this by cross validation

tain test==

k fold cross validation

how many times fold k =5 5 times fold or spit

yellow cover for test

1 part for testing yellowother for traing green

every time testing part changes

leave on out of cross validation

if 100 sample then leave 1 for test remaining 99 % for training

difference between k fold cross validation and k fold validation

after cross val score also same accuracy score and not much difference between training and test result then it means no chance for overfittings

bias====if he always says same thing it is rainy day it is underfitting then it is bais saying same thing

variance== always fluctuate one week one pediction other week different prediction then it is called variance it is example of overfitting

bias trade off== we have to find prediction between underfitting and overfitting by hyperparameter tunning

==========================================================

**What is machine learning, and how does it differ from traditional programming**?

**Explain the difference between supervised and unsupervised learning**.

**What is the bias-variance tradeoff in machine learning**?

**Describe the steps involved in a typical machine learning workflow**.

**What is overfitting in machine learning, and how can it be addressed**?

**What evaluation metrics are commonly used for classification problems**?

**Explain the concept of cross-validation and why it is useful**.

**Describe different feature selection techniques in machine learning**.

**What are the advantages and disadvantages of using decision trees for modeling**?

**Explain the difference between bagging and boosting in ensemble learning**.

**What is the purpose of regularization in machine learning, and how does it work**?

**Describe how the k-means clustering algorithm works**.

**What are the differences between L1 and L2 regularization**?

**Explain the concept of gradient descent and its role in optimizing machine learning models**.

**What are support vector machines (SVMs) and how do they work**?

**Describe the working principle of a neural network**.

**What is backpropagation and how does it relate to training neural networks**?

**Explain the concept of dimensionality reduction and give examples of techniques used for it**.

**What is the ROC curve, and how is it used to evaluate binary classification models**?

**Discuss the challenges and considerations when working with imbalanced datasets**.

## ADVANCED MACHINE LEARNING

========================================================

**Explain the bias-variance tradeoff and how it relates to overfitting and underfitting**.

**What is the difference between bagging and boosting techniques in ensemble learning**?

**Describe the working principle behind support vector machines (SVMs) and how they handle non-linear data**.

**What is the concept of regularization in machine learning, and how does it prevent overfitting**?

**Explain the difference between generative and discriminative models. Provide examples of each**.

**What is deep learning, and how does it differ from traditional machine learning algorithms**?

**Discuss the differences between unsupervised, supervised, and semi-supervised learning**.

**Describe the concept of reinforcement learning and provide an example of how it can be applied in real-world scenarios**.

**Explain the working principle of recurrent neural networks (RNNs) and their applications**.

**How does gradient boosting differ from traditional gradient descent optimization algorithms**?

**What are the challenges associated with training deep neural networks, and how can they be addressed**?

**Discuss the concept of transfer learning and its benefits in machine learning tasks**.

**Explain the concept of autoencoders and their applications in dimensionality reduction and anomaly detection**.

**What is the attention mechanism in deep learning, and how does it improve model performance**?

**Describe the concept of word embeddings, such as Word2Vec or GloVe, and how they are generated**.

====================================================

51 Essential Machine Learning Interview Questions and Answers

https://www.educative.io/blog/top-machine-learning-interview-questions

https://www.mygreatlearning.com/blog/machine-learning-interview-questions/

Top 50 Machine Learning Interview Questions in 2023

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