Weather Forecast Project In Python With Source Code

Weather Forecast Project In Python With Source Code

The Weather Forecast Project In Python was developed using Python Programming, this Simple Project With Source Code created using console based, and this project is good for the beginners or the students who wants to learn programming specially python programming language.

A Weather Prediction Project In Python is a simple console based application using machine learning which helps to determine if the current situation of weather is good to play or not. It is done under the supervised learning in which data are given first to train the system and then the result for new data will be produce.

This Weather Prediction Python Code also includes a downloadable, Project With Source Code for free, just find the downloadable source code below and click to start downloading.

By the way if you are new to python programming and you don’t know what would be the the Python IDE to use, I have here a list of Best Python IDE for Windows, Linux, Mac OS that will suit for you. I also have here How to Download and Install Latest Version of Python on Windows.

To start executing Weather Forecast Project In Python With Source Code, make sure that you have installed Python 3.9 and PyCharm in your computer.

Weather Forecast Project In Python With Source Code : Steps on how to run the project

Installed Libraries

from functools import reduce
import pandas as pd
import pprint

Complete Source Code

from functools import reduce
import pandas as pd
import pprint

class Classifier():
    data = None
    class_attr = None
    priori = {}
    cp = {}
    hypothesis = None


    def __init__(self,filename=None, class_attr=None ):
        self.data = pd.read_csv(filename, sep=',', header =(0))
        self.class_attr = class_attr

    '''
        probability(class) =    How many  times it appears in cloumn
                             __________________________________________
                                  count of all class attribute
    '''
    def calculate_priori(self):
        class_values = list(set(self.data[self.class_attr]))
        class_data =  list(self.data[self.class_attr])
        for i in class_values:
            self.priori[i]  = class_data.count(i)/float(len(class_data))
        print ("Priori Values: ", self.priori)

    '''
        Here we calculate the individual probabilites 
        P(outcome|evidence) =   P(Likelihood of Evidence) x Prior prob of outcome
                               ___________________________________________
                                                    P(Evidence)
    '''
    def get_cp(self, attr, attr_type, class_value):
        data_attr = list(self.data[attr])
        class_data = list(self.data[self.class_attr])
        total =1
        for i in range(0, len(data_attr)):
            if class_data[i] == class_value and data_attr[i] == attr_type:
                total+=1
        return total/float(class_data.count(class_value))

    '''
        Here we calculate Likelihood of Evidence and multiple all individual probabilities with priori
        (Outcome|Multiple Evidence) = P(Evidence1|Outcome) x P(Evidence2|outcome) x ... x P(EvidenceN|outcome) x P(Outcome)
        scaled by P(Multiple Evidence)
    '''
    def calculate_conditional_probabilities(self, hypothesis):
        for i in self.priori:
            self.cp[i] = {}
            for j in hypothesis:
                self.cp[i].update({ hypothesis[j]: self.get_cp(j, hypothesis[j], i)})
        print ("\nCalculated Conditional Probabilities: \n")
        pprint.pprint(self.cp)

    def classify(self):
        print ("Result: ")
        for i in self.cp:
            print (i, " ==> ", reduce(lambda x, y: x*y, self.cp[i].values())*self.priori[i])

'''    Exit from the system it the input is "x" or "exit"   '''
def exitSystem():
        print("System Terminated!")
        print("Thank you for using this system!")
        exit()

if __name__ == "__main__":
    c = Classifier(filename="dataset.csv", class_attr="Play")
    print("Enter the correct values shown in the option! *Case Sensitive")
    print("Enter 'x' or 'exit' to exit from the system")
    outlook = input("Whats the weather outside? (Sunny, Rainy, Overcast):")
    if outlook.lower() == 'x' or outlook.lower() == 'exit':
        exitSystem()
    temp = input("Whats the temperature today? (Hot, Mild, Cool):")
    if temp.lower() == 'x' or temp.lower()== 'exit':
        exitSystem()
    humidity = input("Whats the humidity? (High, Normal):")
    if humidity.lower() == 'x' or humidity.lower()== 'exit':
        exitSystem()
    windy = input("Is it windy tody? (t or f):")
    if windy.lower() == 'x' or windy.lower()== 'exit':
        exitSystem()

    c.hypothesis = {"Outlook":outlook, "Temp":temp, "Humidity":humidity , "Windy":windy}
    c.calculate_priori()
    c.calculate_conditional_probabilities(c.hypothesis)
    c.classify()

Output

Download Source Code below

Summary

A simple machine learning project done in Python. This is a simple console based application using machine learning which helps to determine if the current situation of weather is good to play or not.

It is done under the supervised learning in which data are given first to train the system and then the result for new data will be produce.

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Inquiries

If you have any questions or suggestions about Weather Forecast Project In Python With Source Code, please feel free to leave a comment below.

Technology stack and requirements

To run this Python project on your development machine, you need:

• Python 3.10 or higher. Download from python.org or install via Anaconda if you prefer bundled packages.
• pip package manager. Comes with Python. Used to install project dependencies from requirements.txt.
• Virtual environment. Use venv or conda to isolate project dependencies from your global Python install.
• VS Code or PyCharm. Free code editors with Python syntax highlighting, IntelliSense, and debugging.
• Git. For version control and cloning source code repositories.

Installing the source code

1. Download or clone the repository. Get the ZIP archive from the download link on this page and extract it.
2. Create a virtual environment. Open a terminal in the project folder and run: python -m venv venv, then activate it (venv\Scripts\activate on Windows or source venv/bin/activate on Mac/Linux).
3. Install dependencies. Run pip install -r requirements.txt to install all libraries the project needs.
4. Configure environment variables. If the project uses API keys (OpenAI, Anthropic, database), create a .env file and set the required keys.
5. Run the project. Follow the run command in the README (usually python main.py or streamlit run app.py).

Using this project for your BSIT capstone

• Chapter 1 (Introduction). Discuss the real-world problem this system solves. Cite Philippine or international use cases where the manual process could be automated.
• Chapter 2 (RRL). Compare your project against 5-10 similar published works. Cite ACM, IEEE, or arXiv papers for academic-standard sources.
• Chapter 3 (Methodology). Document the model architecture, training data, hyperparameters, and evaluation metrics used.
• Chapter 4 (Results). Report accuracy, precision, recall, F1-score, and confusion matrix. Screenshot the running app on real inputs.
• Chapter 5 (Conclusion). Identify features for Version 2: better model, larger dataset, mobile deployment, or REST API.

Modules typical of Weather Forecast Project

• Data collection. Historical data from CSV files or API endpoints (Yahoo Finance, Alpha Vantage).
• Data preprocessing. Handle missing values, feature scaling with StandardScaler or MinMaxScaler.
• Feature engineering. Rolling averages, lagged values, technical indicators, seasonal decomposition.
• Model selection. ARIMA/SARIMAX baseline, plus LSTM or Prophet for advanced forecasting.
• Backtesting. Walk-forward validation to test model on unseen historical windows.
• Visualization. Matplotlib or Plotly charts showing actual vs predicted values.

Common enhancements for capstone review

• Multi-variate input. Add related features (news sentiment, macro indicators) to improve predictions.
• Uncertainty quantification. Report prediction intervals, not just point estimates.
• Real-time updates. Schedule daily model retraining as new data arrives.
• Alert system. Email or SMS notification when predictions cross a defined threshold.

Angel Jude Suarez

Full-Stack Developer at PIES IT Solution

Focuses on Python development, machine learning, and AI integration. Has built production AI systems including OpenAI Whisper integration for medical transcription and GPT-4o-powered diagnosis assistance. Strong background in pandas, scikit-learn, and TensorFlow.

Expertise: Python · PHP · Java · VB.NET · ASP.NET · Machine Learning · AI Integration · OpenCV · Django · CodeIgniter
 · View all posts by Angel Jude Suarez →