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DMML Lab Report 01

This lab report for CSE326 details the basic implementation of data mining and machine learning techniques using Python in Google Colab. It includes code snippets for data manipulation with pandas and numpy, demonstrating operations such as reading a CSV file, slicing data, and checking for null values. The report is submitted by Fardus Alam to Lecturer Sadman Sadik Khan at Daffodil International University.

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18 views9 pages

DMML Lab Report 01

This lab report for CSE326 details the basic implementation of data mining and machine learning techniques using Python in Google Colab. It includes code snippets for data manipulation with pandas and numpy, demonstrating operations such as reading a CSV file, slicing data, and checking for null values. The report is submitted by Fardus Alam to Lecturer Sadman Sadik Khan at Daffodil International University.

Uploaded by

Atick Arman
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Lab report

Course code: CSE326


Course Title: Data Mining and Machine Learning Lab
Lab report: 01
Topic: Basic Implementation.

Submitted To:
Name: Sadman Sadik Khan
Designation: Lecturer
Department: CSE
Daffodil International University

Submitted By:
Name: Fardus Alam
ID: 222-15-6167
Section: 62-G
Department: CSE
Daffodil International University

Submission Date: 15-03-2025


Code:
1. from google.colab import drive
2. drive.mount('/content/drive')
3.

Explanation:
Here I mount (connect) my google drive with google colab.

Code:
1. import pandas as pd
2. import numpy as np
3. data = pd.read_csv('/content/drive/MyDrive/lab dataset data
mining/healthcare-dataset-stroke-data2.csv')
4.

Explanation:
Import necessary libraries pandas and numpy for further code execution. And read the csv file from
google drive in data variable.

Code:
1. data.head()
2.

Output:
Explanation:
Showing data rows from “data” frame from beginning of the data set. By default, first 5 rows are
showing.

Code:
1. data.tail()
2.

Output:

Explanation:
Showing last 5 rows by default from the “data” frame.

Code:
1. data.loc[4]
2.

Output:
Explanation:
loc() is a label based method. Here showing specific row = 4.

Code:
1. data.loc[10:15]
2.

Output:

Explanation:
Slicing rows from 10 to 15. loc() method include end label.

Code:
1. data.loc[0:4,'age']
2.

Output:
Explanation:
Slicing rows from 0 to 4(included) from specific column “age”

Code:
1. data.loc[20:24,['gender','bmi']]
2.

Output:

Explanation:
Slicing and showing rows from 20 to 24(included) from data frame with two specific columns “gender”,
“bmi”.

Code:
1. data.iloc[0:4,0:3]
2.

Output:
Explanation:
iloc() is a index based method and it exclude the end index in slicing. Here I slice rows from 0 to 4
(exclude) and columns from 0 to 3 (not include).

Code:
1. data.dtypes
2.

Output:

Explanation:
Showing the data types of all columns from data frame “data”.

Code:
1. data.info()
2.
Output:

Explanation:
info() method give overall information (Non-Null Count and Data type of each columns) of the data set.

Code:
1. data.isnull().sum()
2.
Output:

Explanation:
Showing the null (missing) values of each column. Here “age” has 64 and “bmi” has 201 null values.

Code:
1. print(f'bmi column maximum value = {max(data.bmi)}')
2. print(f'age column minimum value = {min(data.age)}')
3.

Output:

Explanation:
Showing “bmi” column maximum value and minimum value of “age” column.
Code:
1. pd.unique(data['work_type'])
2.

Output:

Explanation:
Showing the unique values of “work type” column.

Code:
1. data['gender'].value_counts()
2.

Output:

Explanation:
Showing counts of each unique values of “gender” column.

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