Internship Report on Python For Data Science/ Iris Dataset PDF 2024-2025

Summary

This internship report delves into the application of Python for data science using the Iris dataset. The report includes elements of data exploration, preprocessing, model selection, and the application of various machine learning algorithms. It covers use of libraries such as NumPy, Pandas, and Scikit-Learn.

Full Transcript

**Internship Report** **on** **Python For Data Science/ Iris Dataset** **BACHELOR OF ENGINEERING** **in** **Submitted By: Pooja** **Batch: 2023-2027** **Panipat Institute of Engineering & Technology, (Affiliated to Kurukshetra University Kurukshetra, India)** **Samalkha, Panipat** **(2024-2...

**Internship Report** **on** **Python For Data Science/ Iris Dataset** **BACHELOR OF ENGINEERING** **in** **Submitted By: Pooja** **Batch: 2023-2027** **Panipat Institute of Engineering & Technology, (Affiliated to Kurukshetra University Kurukshetra, India)** **Samalkha, Panipat** **(2024-2025)** [\ ] Acknowledgement =============== I would like to express my deepest gratitude to **Miss Shivangi Goyal** for their constant support, valuable guidance, and encouragement throughout the internship project. Their insights and expertise were instrumental in the successful completion of this project. I am also thankful to the faculty members of the Panipat Institute of Engineering & Technology for their academic and technical guidance, which greatly assisted me in understanding complex concepts and methodologies. ABSTRACT ======== **CONTENT** CHAPTER NO. DESCRIPTION PAGE NO. ------------- ----------------------------------------- ---------- 1 Introduction 1-5 1.1 Overview of data-science and python 1.2 What is Iris dataset 1.3 Objectives of project 2 Software and hardware requirements 6-11 2.1 Hardware requirements 2.2 Software requirements 3 Software requirement analysis 12-17 3.1 Functional requirements 3.2 Non-Functional requirements 4 Coding templates 18-24 4.1 Importing Libraries 4.2 Loading the Dataset 4.3 Exploratory Data Analysis 4.4 Data preprocessing 4.5 Model selection and training 5 Graphical representation 25-28 6 Output screen 29-36 6.1 Exploratory Data Analysis output 6.2 Model performance output 7 Conclusion 37-40 8 References 41 **Figure no.** **Figure name** **Page no.** ---------------- ----------------- -------------- 1.1 Setosa 2 1.2 Versicolor 2 1.3 Virginica 2 List of Graphs ============== -- ----------- ---- 26 Histogram -- ----------- ---- **Abbreviation** **Full Form** ------------------ ------------------------------------ EDA Exploratory Data Analysis UML Unified Modeling Language DFD Data Flow Diagram SVM Support Vector Machine KNN K-Nearest Neighbors RF Random Forest PCA Principal Component Analysis API Application Programming Interface IDE Integrated Development Environment

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