The recommendation system is prove to be very helpful in increasing revenue for big platforms like Netflix, YouTube, Amazon, Flipkart etc. Users are also introduced with items they like and they don't have to rigorously search for it. There is one method for recommendation system called Collaborative Filtering. It's of two types :- User-based collaborative … Continue reading Collaborative Filtering
K Means cluster , Decision tree , SVM
K Means cluster This a unsupervised machine learning algorithm. It form data into K clusters. Cluster are formed by choosing centroid. At first random centroid is picked. Iterating the process until centroid changes no more. Decision Tree In this algorithm flowchart of decision is formed on basis of that prediction is done. Every feature chosen … Continue reading K Means cluster , Decision tree , SVM
Machine Learning
Machine Learning is basically learning patterns and making prediction. There are basically two types of machine learning algorithms :- Supervised learningUnsupervised learning Supervised Learning In this type of machine learning algorithm we provide labels or target values. For example :- we give a picture of dog and also label it that it is dog. Unsupervised … Continue reading Machine Learning
Regression
We are going to discuss :- Linear regressionPolynomial regressionMultivariate regression Linear Regression It is very simple machine learning algorithm. In this we try to fit a straight line on our data sample. y = m*x+c This algorithm finds best fit values for m and c which are slope and intercept of the line. PolynomialIt is … Continue reading Regression
Conditional Probability
Probability means how likely an event can occur and conditional probability is how likely an event can occur given another event has already occurred. There is a dependence between the events. P(B/A) = P(A,B)/P(A) P(B/A) - Probability of B given A P(A,B) - Probability of both B and A P(A) - Probability of A For … Continue reading Conditional Probability
Covariance And Correlation
Covariance It gives relation between two features or two random variables. It tells how one depends upon other. It could be positive or negative. Simple way to find covariance is to multiple variance of both the variables. Let's discuss this with an example then it will be more clear. Let a = [1,2,3,4,5,6,7,8,9] and b … Continue reading Covariance And Correlation
Getting Started With Matplotlib
Data Visualization is very important part of data science. It helps to explain our data to others. It helps us in understanding our data well. Without data visualization it is difficult to get insights of the data. So let's get started with data visualization. Matplotlib is go to library for data visualization. Let do some … Continue reading Getting Started With Matplotlib
Percentile and Moments
Percentile It gives the level of an element in data. Lets discuss with an example :- 1,2,3,4,5,6,7,8,9 ,10 we have this data in increasing order. To find Kth percentile :- Step 1 : Arrange the elements in ascending order Step 2 : multiple K percent with number of elements Step 3 : Let result be … Continue reading Percentile and Moments
Variance and Standard Deviation
Let's discuss variance and standard deviation. Variance It is average of squared differences from the mean. In simple words we subtract mean from every element and which give us the distance of every element from mean. Then we square each to avoid negative numbers and then find average. For example :- 1,2,3,4,5,6,7,8,9 Mean = 5 … Continue reading Variance and Standard Deviation
Mean, Median and Mode
Let's get into little elementary stuff which is basic but important. Mean It is the average of all entries or data points. For example :- 9,2,3,1,2,2,4,1,5,6,3 Mean = (9+2+3+1+2+2+4+1+5+6+3)/11 = 3.455 Median It is the mid point of the data. For example :- considering previous data i.e 9,2,3,1,2,2,4,1,5,6,3 Median = sort the data then pick … Continue reading Mean, Median and Mode
Data Science 