Machine learning

 

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. 

 

Machine learning algorithms use historical data as input to predict new output values.

 

Applications of Machine Learning:

•Dynamic Pricing

•Product recommendations

•Traffic prediction

•Self-driving cars

•Email Spam and Malware Filtering

 

 

 

Regression models

•What is regression??

•Regression can be said to be a technique to find out the best relationship between the input variables known as predictors and the output variable also known as response/target variable.

•Best relationship is signified by minimal difference between the predicted and the actual values.

•Regression analysis is an important tool for modelling and analyzing data. Here, we fit a curve / line to the data points, in such a manner that the differences between the distances of data points from the curve or line is minimized.

•It indicates the strength of impact of multiple independent variables on a dependent variable.

 

 

Linear regression

•It is a supervised learning algorithm mostly used in predictive analysis which typically means trying to fit the best straight line between the input and output variables in order to model our data.

•this best fitting straight line is also known as regression line that minimizes the sum of the squared errors of prediction.

•Characteristic of linear regression is the output variable should be continuous.

•Linear Regression establishes a relationship between dependent variable (Y) and one or more independent variables (X) using a best fit straight line (also known as regression line).

 

 

 

 

 

 

Best fit - line or curve:

1. The maximum number of points covered.

2. Minimize the distance between other points (error -SSE)

 

 

 

 

 

 

 

The below-given equation is used to denote the linear regression model:

y=mx+c+e

where m is the slope of the line, c is an intercept, and e represents the error in the model

Linear regression where Y is the output variable and X is the input variable/variables.

 

 

 Find Slope & Intercept:

 

 

 

 Linear Regression Python code:

#Importing Needed packages import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model     #Reading the data in path='C:/TrainingDocs/MachineLearningwithPython/homeprices1.csv' df = pd.read_csv(path) df   # summarize the data df.describe()   #plot graph for datapoints %matplotlib inline plt.xlabel('area(sqr ft)') plt.ylabel('price(US$)') plt.scatter(df.Area,df.Price,color='red',marker='+')     #Using sklearn package to model data #fitting training data and then generating predictions on test data reg=linear_model.LinearRegression() reg.fit(df[['Area']],df.Price)     #Predict price for area (3300 sq feet) reg.predict([[3300]])   #Plot graph for prediction #Draw the line on the scatter plot %matplotlib inline plt.xlabel('Area',fontsize=20) plt.ylabel('Price',fontsize=20) plt.scatter(df.Area,df.Price,color='red',marker='+') plt.plot(df.Area,reg.predict(df[['Area']]),color='blue')     print ('Coefficients: ', reg.coef_) print ('Intercept: ',reg.intercept_)   #Check Value of Coefficients reg.coef_   #Check Value of intercept reg.intercept_   #Validate linear equation #y=mx+b #134.07534247*3300+176232.87671232875 134.07534247*3300+176232.87671232875   #Predict price based on given area path1='C:/TrainingDocs/MachineLearningwithPython/areas.csv' d= pd.read_csv(path1) d.head(3)   p=reg.predict(d) d['prices'] = p path2='C:/TrainingDocs/MachineLearningwithPython/prediction.csv' d.to_csv(path2,index=False)

 

Multiple linear regression:

 

Predict for 3000 sq ft, 3 bedrooms, 40-year-old

 

 Multiple linear regression Python Code:

#Importing Needed packages import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model       #Reading the data in path='C:/TrainingDocs/MachineLearningwithPython/homeprices1.csv' df = pd.read_csv(path) df   #Cleaning of data import math median_bedrooms=math.floor(df.Bedrooms.median()) median_bedrooms   #Assign some value to NaN   df.Bedrooms=df.Bedrooms.fillna(median_bedrooms) df     #To Train model reg=linear_model.LinearRegression() reg.fit(df[['Area','Bedrooms','Age']],df.Price)       print ('Coefficients: ', reg.coef_) print ('Intercept: ',reg.intercept_)       #Predict for 3000 sq ft, 3 bedrooms, 40 yeal old reg.predict([[3000,3,40]])     #Validate multiple equation #y=m1x1+m2x2+m3x3+y #3000*137.25+3*-26025+40*-6825+383724.9999999998 3000*137.25+3*-26025+40*-6825+383724.9999999998

How to optimize Your Google My Business Listing

Google My Business (GMB) – the free tool from Google that helps business owners manage their online presence across the search engine and its growing portfolio of utilities – offers the greatest impact for brands seeking local exposure.

Google My Business complements your existing website by giving your business a public identity and presence with a listing on Google, the most popular search engine in the world.

The information you provide about your business can appear in Google Search, Google Maps, and on Google+.

1. Provide Complete Data for Your Listing :

Local search results favor the most relevant results for searches, and business offering the most detailed and accurate information will be easier to serve in search.
Don’t leave anything to be guessed or assumed; make sure your listing communicates with potential customers what your business does, where it is, and how they can acquire the good and/or services your business is offering.

2. Power of including Keywords :

You need to add relevant keyword in the business description.Also,You can do rearch of comtitores keywords using below link:

www.semrush.com
www.spyfu.com

3. Customer Reviews:

You need to request your customer to share feedback of your service by providing reviews.
Positive reviews are going to have a positive effect on potential customers when researching your business, but they also increase your business’s visibility in search results.


4. Local Citations:

You need to register your business in local business directories (Name,Address,Phone Nuumber ) Examples : Yellowpages,Yelp,Foursquare )

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AWS Machine Learning Scholarship Program

AWS Machine Learning Scholarship Program

AWS and Udacity are collaborating to educate developers of all skill levels on Machine Learning concepts.

The foundations course is intended to help developers of all skill levels get started with machine learning. When you successfully complete the course, you’ll be awarded with a certificate of completion.

At the end of the AWS Machine Learning Foundations Course, learners will take an assessment from which top students will be selected for one of 325 follow-up scholarships to one of Udacity’s most popular Nanodegree programs: AWS Machine Learning Engineer.

Enrollment Opens : Tuesday, May 19                                  

Enrollment Closes: Wednesday, July 31, 11:59 PM PST

Registration Link:

SQL Queries for Interview

Q1 SQL select total and split into success and failed

Table 1                Table 2                   
|leadid|Location|      |leadid|leadstatus|       
|---------------|      |-----------------|
|1     |Japan   |      |1     | Hired    |
|2     |China   |      |2     | Failed   |
|3     |Korea   |      |3     | Hired    |
|4     |Japan   |      |4     | Hired    |
|5     |Japan   |      |5     | Hired    |

|Location|Interview|Hired|Failed|
|-------------------------------|
|Japan   | 3       |3    |0     |
|Korea   | 1       |1    |0     |
|China   | 1       |0    |1     |

SELECT Location, COUNT(*) as Interview,
SUM(CASE WHEN leadstatus = 'Hired' THEN 1 ELSE 0 END) as Hired,
SUM(CASE WHEN leadstatus = 'Failed' THEN 1 ELSE 0 END) as Failed
FROM table1 
LEFT JOIN table2 ON table1.leadid = table2.leadid
            GROUP BY Location 
            ORDER BY Interview DESC

Select location,count(*) as Interview,
SUM(CASE WHEN (status='Hired')Then 1 Else 0 END) as Hired,
SUM(CASE WHEN(status='Failed') Then 1 Else 0 END) as Failed 
from loc inner join status on loc.leadid= status.leadid 
group by location;

Q 2 Second maximum salary 

Second maximum salary using sub query and IN clause 

mysql> SELECT max(salary) FROM Employee WHERE salary NOT IN 

(SELECT max(salary) FROM Employee);


mysql> SELECT max(salary) FROM Employee WHERE salary < 

(SELECT max(salary) FROM Employee);


Second highest salary using TOP keyword of Sybase or SQL Server database


SELECT TOP 1 salary FROM ( SELECT TOP 2 salary FROM employees ORDER BY salary DESC)

 AS emp ORDER BY salary ASC

Second maximum salary using LIMIT keyword of MYSQL database

SELECT salary  FROM (SELECT salary FROM Employee ORDER BY salary DESC LIMIT 2)

AS emp ORDER BY salary LIMIT 1;



 Q 3 Write an SQL query to clone a new table from another table.

 SELECT * INTO WorkerClone FROM Worker; (With data )
 SELECT * INTO WorkerClone FROM Worker WHERE 1 = 0; (Without data )
 CREATE TABLE WorkerClone LIKE Worker; (Without data )

Hadoop HDFS Commands

Hadoop HDFS Commands

ls

Returns all the available files and subdirectories present under the root directory.
hadoop fs –ls /

cd  C:\hadoop-2.8.0\data\namenode
hadoop fs -mkdir  test1
hadoop fs -ls \
hadoop fs -mkdir -p /test1/Hadoop
hadoop fs -mkdir  /test1/Hadoop/tweeter_data

copyFromLocal
Copy a file from local filesytem to HDFS location.
hadoop fs –copyFromLocal Sample1.txt /test1/Hadoop/tweeter_data

put –
This hadoop command uploads a single file or multiple source files from local file system to hadoop distributed file system (HDFS).
hadoop fs –put Sample2.txt /test1/Hadoop/tweeter_data

moveFromLocal
This hadoop command functions similar to the put command but the source file will be deleted after copying.
hadoop fs –put Sample3.txt /test1/Hadoop/tweeter_data

du
Displays the disk usage for all the files available under a given directory.
hadoop fs –du /test1/Hadoop/

df
 Displas disk usage of current hadoop distributed file system.
hadoop fs –df

Expunge
This HDFS command empties the trash by deleting all the files and directories.
hadoop fs –expunge

Cat
This is similar to the cat command in Unix and displays the contents of a file.
hadoop fs –cat /test1/Hadoop/tweeter_data/Sample3.txt

cp
Copy files from one HDFS location to another HDFS location.
hadoop fs –cp /test1/Hadoop/tweeter_data/  /test2/

mv
Move files from one HDFS location to another HDFS location.
hadoop fs –mv /test2/  /test12/

rm
Removes the file or directory from the mentioned HDFS location.
hadoop fs –rm -r /test12/

tail
This hadoop command will show the last kilobyte of the file to stdout.

hadoop fs -tail /test1/Hadoop/tweeter_data/Sample3.txt

touchz
This command can be used to create a file of zero bytes size in HDFS filesystem.
hadoop fs -touchz /test1/URI.txt

tail
This command is used to show the last 1KB of the file.
hadoop fs -tail /test1/Hadoop/tweeter_data/Sample3.txt

count
This command is used to count the number of directories, files and bytes
hadoop fs -count /test1/Hadoop/