Menu of our Cafe



  Hello People,

  Welcome to our new cafe named as DATACAFE. I am Chirag Gupta Working as a Data Scientist  going to start a Data Science content for you .I hope you guys enjoy it .In this journey we will cover all topics related to data science concepts & interviews on daily basis.


Lets Discuss Menu:

  • PYTHON(Pandas, Numpy)
  • SQL
  • STATISTICS
  • MACHINE LEARNING
  • POWER BI
  • TABLEAU


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Overfitting , Underfitting Bias & Variance Understanding by CHIRAG

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  What is Overfitting? Overfitting means model has High accuracy score on training data but low accuracy score on test data. (That means our model best fit line satisfied all the training data points . ie. Low Training Error & High Testing Error & High Training Accuracy & Low Test Accuracy ) Reasons for Overfitting:   High variance(High Test Error) .  Low Bias(Low Training Error). The model is too complex. The size of the training data. What is Bias? Bias is the difference between the average prediction of our model and the correct value which we are trying to predict. A model with high bias pays very little attention to the training data and oversimplifies the model. Let’s assume we have trained the model and are trying to predict values with input ‘x_train’. The predicted values are y_predicted.  Bias is the error rate of y_predicted and y_train. In simple terms.   think of bias as the error rate of the training data. When the error rate is high,...
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Linear Regression Understanding by CHIRAG

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  Linear Regression 1 .)Linear regression is a supervised learning algorithm used when target / dependent variable continues real number. 2.)Linear regression is a type of statistical analysis used to predict the relationship between dependent and independent Variables using BEST FIT LINE. 3.) It work on the principle of ordinary least square (OLS)/ Mean square errror (MSE) Simple Linear Regression In a simple linear regression, there is one independent variable and one dependent variable. The model estimates the slope and intercept of the line of best fit, which represents the relationship between the variables. The slope represents the change in the dependent variable for each unit change in the independent variable, while the intercept represents the predicted value of the dependent variable when the independent variable is zero. Mathematically, we can represent a linear regression as: y= a0+a1x+ ε Y= Dependent Variable (Target Variable) X= Independent Variable (predictor Variab...
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Understanding Confusion Matrix by CHIRAG

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What Is a Confusion Matrix? A confusion matrix is a performance evaluation tool in machine learning, representing the accuracy of a classification model.  It displays the number of true positives, true negatives, false positives, and false negatives. A Confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the total number of target classes.     For a binary classification problem, we would have a 2 x 2 matrix, as shown below, with 4 values: Let’s understanding this matrix: The target variable has two values: 0 & 1 The  columns  represent the  Predicted values  of the target variable The  rows  represent the  Actual values  of the target variable. But wait – what’s TP, FP, FN, and TN here? That’s the crucial part of a confusion matrix. Let’s understand each term below. Important Terms in a Confusion Matrix True Positive (TP)  The predicted value matches the actual value...
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