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Aalekh Agnihotri
1900910310001
Mentor : Dr. Arun Kumar Gowdru
Internship Presentation
Data Science and And Machine Learning
Using Python
DEPARTMENT OF ELECTRONICS AND COMMUNICATION
ENGINEERING

OBJECTIVE
❖ The goal of data science is to construct the means for extracting business-
focused insights from data. This requires an understanding of how value
and information flows in a business, and the ability to use that
understanding to identify business opportunities.
❖ Data Science using python will emphasize you in gaining knowledge about
machine learning, data visualization, web scraping, & natural language
processing.
❖ Machine learning is a sub-category of artificial intelligence and effectively
automates the process of analytical model building and allows machines to adapt
to new scenario independently.
❖ Machine learning is the study of algorithm that :
● Improves their Performance P
● At some task T
● With Experience E

Libraries and Packages
• To understand machine learning, you need to
have basic knowledge of Python
programming. In addition, there are a number
of libraries and packages generally used in
performing various machine learning tasks as
listed below:
– numpy - is used for its N-dimensional array
objects
– pandas – is a data analysis library that
includes data frames
– matplotlib – is 2D plotting library for creating
graphs and plots
– scikit-learn - the algorithms used for data
analysis and data mining tasks

LIFE CYCLE OF DATA SCIENCE IN
VARIOUS FIELDS
Data science is a "concept to unify statistics,
data analysis and their related methods" in
order to "understand and analyze
actual phenomena" with data.

Uses of Machine Learning
❖Traffic prediction
❖Virtual personal assistant
❖Speech Recognition
❖E-Mail spam and malware filtering
❖Face Detection
❖Weather Prediction

MY PROJECT
Topic : Estimated Selling Price of a Car
from flask import Flask, render_template, request
import jsonify
import requests
import pickle
import numpy as np
import sklearn
from sklearn.preprocessing import StandardScaler
app = Flask(__name__)
model = pickle.load(open('random_forest_regression_model.pkl',
'rb'))
@app.route('/',methods=['GET'])
def Home():

return render_template('index.html')
standard_to = StandardScaler()
@app.route("/predict", methods=['POST'])
def predict():
Fuel_Type_Diesel=0
if request.method == 'POST':
Year = int(request.form['Year'])
Present_Price=float(request.form['Present_Price'])
Kms_Driven=int(request.form['Kms_Driven'])
Kms_Driven2=np.log(Kms_Driven)
Owner=int(request.form['Owner'])
Fuel_Type_Petrol=request.form['Fuel_Type_Petrol']
if(Fuel_Type_Petrol=='Petrol'):
Fuel_Type_Petrol=1
Fuel_Type_Diesel=0

else:
Fuel_Type_Petrol=0
Fuel_Type_Diesel=1
Year=2020-Year
Seller_Type_Individual=request.form['Seller_Type_Individual
']
if(Seller_Type_Individual=='Individual'):
Seller_Type_Individual=1
else:
Seller_Type_Individual=0
Transmission_Mannual=request.form['Transmission_Mannual']
if(Transmission_Mannual=='Mannual'):
Transmission_Mannual=1
else:
Transmission_Mannual=0
prediction=model.predict([[Present_Price,Kms_Driven2,Owner,
Year,Fuel_Type_Diesel,Fuel_Type_Petrol,Seller_Type_Individu
al,Transmission_Mannual]])

output=round(prediction[0],2)
if output<0:
return
render_template('index.html',prediction_texts="Sorry you
cannot
sell this car")
else:
return
render_template('index.html',prediction_text="You Can Sell
The Car at {}".format(output))
else:
return render_template('index.html')
if __name__=="__main__":
app.run(debug=True)

This image is of a web page developed
by our program.So if we fill this web
page , it will predict us the appropriate
car model and the selling price of that
car .
Also , all type of data are not applicable
,i.e.,it is little bit inaccurate..

CONCLUSION
Data science education is well into its formative stages of development; it is evolving
into a self-supporting discipline and producing professionals with distinct and
complementary skills relative to professionals in the computer, information, and
statistical sciences. However, regardless of its potential eventual disciplinary status,
the evidence points to robust growth of data science education that will indelibly
shape the undergraduate students of the future. In fact, fueled by growing student
interest and industry demand, data science education will likely become a staple of
the undergraduate experience.

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