https://github.com/shaikats/python-assignment
Simulation and Modeling Lab assignments using Python, NumPy, Matplotlib, and Jupyter Notebook.
https://github.com/shaikats/python-assignment
jupyter-notebook matplotlib numpy python simulation-modeling
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Simulation and Modeling Lab assignments using Python, NumPy, Matplotlib, and Jupyter Notebook.
- Host: GitHub
- URL: https://github.com/shaikats/python-assignment
- Owner: Shaikats
- Created: 2025-02-11T19:16:28.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-19T08:58:25.000Z (over 1 year ago)
- Last Synced: 2025-02-19T09:35:00.092Z (over 1 year ago)
- Topics: jupyter-notebook, matplotlib, numpy, python, simulation-modeling
- Language: Jupyter Notebook
- Homepage:
- Size: 265 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 💻 Simulation & Modeling Lab – Assignment Repository
## 👤 **Personal Information**
**Name:** Shahriar Hossain Shaikat
**ID:** 2215151034
**University:** UITS (University of Information Technology and Sciences)
**Department:** Computer Science and Engineering (CSE)
**Batch:** 51
**Section:** 7A2
## 📖 **Course Details**
**Course Code:** CSE 413
**Course Name:** Simulation and Modeling Lab
**Course Teacher:** Audity Ghosh
---
## 📂 **Assignments**
📊 Assignment 1: Data Visualization and Matrix Operations
### 📝 Tasks
1. Generate two vectors with 15 random floats, plot them, and label axes.
2. Create a 4x4 random matrix, visualize as a heatmap, and label rows/columns.
3. Generate two 4x4 matrices, perform arithmetic operations, visualize with bar plots.
### 📌 Concepts Covered
- Random number generation
- Data visualization with Matplotlib
- Matrix operations with NumPy
- Heatmap visualization
- Bar plot representation of matrix computations
📂 **[View Assignment 1](https://github.com/Shaikats/Python-Assignment/blob/main/assignment-01.ipynb)**
🎲 Assignment 2: Random Matrix Generation
### 📝 Tasks
1. Use NumPy to create a 3×3 matrix of random integers between 1 and 50.
2. Run the code twice:
- Without setting a seed, allowing random values to change each time.
3. Observe and explain the differences in outputs (in a different cell as text).
### 📌 Concepts Covered
- Random integer matrix generation
- Effects of using fixed seeds in random number generation
📂 **[View Assignment 2](https://github.com/Shaikats/Python-Assignment/blob/main/assignment-02.ipynb)**
📊 Assignment 3: Statistical Analysis and Hypothesis Testing
### 📝 Tasks
1. **Task 1:** Calculate the t-statistic and p-value for a sample of data and a hypothesized mean.
2. **Task 2:** Interpret the results and visualize the sample mean against the hypothesized mean using a bar plot with error bars.
### 📌 Concepts Covered
- T-statistic and p-value calculation
- Hypothesis testing
- Data visualization (Bar plot, Error bars)
📂 **[View Assignment 3](https://github.com/Shaikats/Python-Assignment/blob/main/assignment-03.ipynb)**
🎲 Assignment 4: Normal Distribution and Two-Sample t-Test
### 📝 Tasks
1. **Task 1:** Randomly generate 50 values from a normal distribution with a chosen mean (μ1) and standard deviation.
2. **Task 2:** Randomly generate 50 values from a normal distribution with a different mean (μ2) and standard deviation.
3. **Task 3:** Perform a two-sample t-test to check if the means of the two samples are significantly different.
4. **Task 4:** Visualize the comparison using a boxplot.
### 📌 Concepts Covered
- Normal distribution sampling
- Two-sample t-test (Welch's test)
- Boxplot visualization
📂 **[View Assignment 4](https://github.com/Shaikats/Python-Assignment/blob/main/assignment-04.ipynb)**
📊 Assignment 5: One-Sample Kolmogorov-Smirnov Test on Daily Temperatures
### 📝 Tasks
1. Load the dataset of daily temperatures.
2. Extract temperature scores for analysis.
3. Center and scale the data for normal comparison.
4. Perform One-Sample K-S Test.
5. Output the hypothesis test result, p-value, and KS statistic.
6. Plot the empirical CDF vs the normal CDF.
7. Write a conclusion.
### 📌 Concepts Covered
- Kolmogorov-Smirnov test
- Hypothesis testing for distribution fitting
- Empirical vs theoretical cumulative distribution comparison
📂 **[View Assignment 5](https://github.com/Shaikats/Python-Assignment/blob/main/assignment-05.ipynb)**
📊 Assignment 6: Monte Carlo Simulation & Queueing System Analysis
### 📝 Tasks
#### ✍️ Situation 2 (Monte Carlo Simulation):
Simulate inventory behavior of LatinOrg Inc. over 60 weeks.
- a) Estimate average order size.
- b) Calculate average total cost of having the product.
#### ⌚ Single Server Queueing:
1. Perform a simulation for 200 customers with λ=3 and μ=4. Compare waiting time and server utilization.
2. Simulate a system with λ=8 and μ=6. Discuss impact of server overload.
3. Modify queue with limited capacity (max 10 customers). Analyze performance impact.
### 📌 Concepts Covered
- Monte Carlo Simulation
- Inventory cost modeling
- Queue theory (M/M/1)
- Server utilization, queue length, and system performance
📂 **[View Assignment 6](https://github.com/Shaikats/Python-Assignment/blob/main/assignment-06.ipynb)**
## 📂 **Assignment Links**
[](https://github.com/Shaikats/Python-Assignment/blob/main/assignment-01.ipynb)
[](https://github.com/Shaikats/Python-Assignment/blob/main/assignment-02.ipynb)
[](https://github.com/Shaikats/Python-Assignment/blob/main/assignment-03.ipynb)
[](https://github.com/Shaikats/Python-Assignment/blob/main/assignment-04.ipynb)
[](https://github.com/Shaikats/Python-Assignment/blob/main/assignment-05.ipynb)
[](https://github.com/Shaikats/Python-Assignment/blob/main/assignment-06.ipynb)
---
## 🎯 **What I Learned**
- How to generate and manipulate random numerical data in Python.
- Effective use of Matplotlib for visualizing mathematical operations.
- Understanding of matrix operations and their effects.
- Hypothesis testing with t-statistics, p-values, and K-S test.
- Best practices for structuring a professional GitHub repository.
- Inventory simulation and queue performance modeling with Monte Carlo.
---
## ⚡ **Run This in Google Colab**
1. Download the `.ipynb` File or Clone the repository:
```bash
git clone https://github.com/shaikats/Simulation-and-Modeling-Lab.git
```
2. Open [Google Colab](https://colab.research.google.com/).
3. Click on **"File" > "Upload Notebook"** and select the `.ipynb` file, or create a new notebook and Paste the Code.