Data analytics refers to the process of examining, cleaning, transforming, and interpreting raw data in order to extract meaningful insights, make informed decisions, and support business or research objectives. It involves using various techniques and tools to uncover patterns, trends, correlations, and other valuable information within large and complex datasets. Data analytics plays a crucial role in a wide range of industries, including business, healthcare, finance, marketing, science, and more. Key components of data analytics include: Data Collection: Gathering data from various sources, which can include structured data (e.g., databases, spreadsheets) and unstructured data (e.g., text, images, videos). Data Cleaning and Preprocessing: This step involves cleaning the data to remove errors, inconsistencies, and duplicate entries. Data preprocessing also includes handling missing values and transforming data into a suitable format for analysis. Data Transformation: Converting and shaping data to make it more suitable for analysis. This might involve aggregation, normalization, encoding categorical variables, and feature engineering. Exploratory Data Analysis (EDA): Exploring the data through statistical and visual techniques to understand its underlying patterns, distributions, and relationships. EDA helps in identifying outliers, understanding data quality, and formulating hypotheses for further analysis. Data Modeling: Developing models and algorithms to analyze the data and make predictions. This can include statistical methods, machine learning algorithms, and other quantitative techniques. Model Training and Validation: If machine learning or predictive modeling is involved, this step includes training models on a subset of the data and validating their performance using separate data to ensure they generalize well to new, unseen data. Data Visualization: Creating meaningful visual representations of the data to communicate insights effectively. Visualizations such as charts, graphs, and dashboards help stakeholders understand trends and patterns more easily. Interpretation and Insight Generation: Analyzing the results of the data analysis to extract meaningful insights and actionable recommendations. This is where the extracted information is turned into actionable knowledge for decision-making. Predictive and Prescriptive Analytics: Using the insights gained from data analysis to make predictions about future outcomes and recommend actions to achieve desired outcomes. Business Impact: Implementing the insights and recommendations into business strategies, processes, and decision-making to drive improvements and achieve specific goals. Continuous Improvement: Data analytics is an iterative process. As new data becomes available and business needs evolve, the analysis process should be refined and updated to stay

1. My INTERN
experience at
Dhatcan academy
S.SACHIN

2. About Dhatchan academy
Bevywise was the IOT based company.
• It was the training institute. They provides Career Oriented Training and Live project training
• They provides internships and Courses.
• Course they offered
Python
Java
Python with AI/ML
Data Analysis
Summer courses
Internships
School / College Projects
• It was managed by BEVYWISE company.

3. My learning’s in Dhatchan
• In Dhatchan we learnt about the linux and linux commands
and we did the python programs.
• We learnt about SOLARLINT.
• And we learnt about SQL querys , my sql connection to DB and
HTML basics.
• They give a lecture about IOT simulator and basic of cloud
servers.

4. Iot Simulator
That was the IOT simulator by these simulator It was used for Automation
and It was Developed by BEVYWISE.

5. 02
Report
WEEK
WEEK
IN week 1 we learnt about basics of python
programmimng and linux commands
And they assigned small programs.
IN Week 2 we leaarnt about the SQL querys
And they assigned the querys to solve.
We learn about MYSQL connection with DB
And they assign task.
01

6. 04
Report
WEEK
WEEK
IN week 3 they take on a lecture About IOT
And gave the task.
They teach about servers and HTML basics.
IN the final Week we do our intern project
work
we done the project in face Recognition.
03

7. Project
Face recognition
It works by identifying and measuring facial features
in an image. Facial recognition can identify human
faces in images

8. Program
import cv2
import face_recognition
# Load a sample image and learn how to recognize it
sample_image =
face_recognition.load_image_file("/content/drive/MyDrive/sa
mple_image.jpg")
sample_encoding =
face_recognition.face_encodings(sample_image)[0]
# Initialize some variables
face_locations = []
face_encodings = []
# Load an image to be recognized
unknown_image =
face_recognition.load_image_file("/content/drive/MyDrive/unk
nown_image.jpeg")
# Find all face locations and encodings in the unknown image
face_locations =
face_recognition.face_locations(unknown_image)
face_encodings =
face_recognition.face_encodings(unknown_image,
face_locations)
# Loop through the faces found in the unknown image
for face_encoding in face_encodings:
# Compare the face encoding with the sample face
encoding
matches =
face_recognition.compare_faces([sample_encoding],
face_encoding)
# If a match is found, print the name
if matches[0]:
print("Match found! This is the sample person.")
else:
print("No match found.")
# Display the image with face locations
for (top, right, bottom, left) in face_locations:
cv2.rectangle(unknown_image, (left, top), (right, bottom), (0,
0, 255), 2)
gray = cv2.cvtColor(unknown_image, cv2.COLOR_RGB2GRAY)
from google.colab.patches import cv2_imshow
cv2_imshow(gray)
cv2.waitKey(0)
cv2.destroyAllWindows()

9. From the model the Sample image and the unknown
image are same so match found.
OUTPUT

10. THANK
YOU