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Weather Forecast Project In Python Django With Source Code

Python weathercast involves predicting things like cloud cover, rain or snow, wind speed, and temperature before they happen. ... We forecast the weather by looking at current conditions, motion of air and clouds, historical patterns, pressure changes, and computer models.

Features Weather Forecast Project In Python Django:

  1. Time to time update weather
  2. Tamprature Update
  3. Last 7 days data Predict
  4. change weather in every hours as according to weather changes.
  5. provide accurate data information about weather.
  6. user can search weather anytime and anywhere.
  7. any places data can be search and provide information as according to weather.
  8. help user to travel.
  9. help User to future plans for holidays.


Weather forecasts are made by collecting as much data as possible about the current state of the atmosphere (particularly the temperature, humidity and wind) and using understanding of atmospheric processes (through meteorology) to determine how the atmosphere evolves in the future.

However, the chaotic nature of the atmosphere and incomplete understanding of the processes mean that forecasts become less accurate as the range of the forecast increases.

Traditional observations made at the surface of atmospheric pressure, temperature, wind speed, wind direction, humidity, precipitation are collected routinely from trained observers, automatic weather stations or buoys.

During the data assimilation process, information gained from the observations is used in conjunction with a numerical model's most recent forecast for the time that observations were made to produce the meteorological analysis.

Numerical weather prediction models are computer simulations of the atmosphere.

They take the analysis as the starting point and evolve the state of the atmosphere forward in time using understanding of physics and fluid dynamics.

The complicated equations which govern how the state of a fluid changes with time require supercomputers to solve them.

The output from the model provides the basis of the weather forecast.

Technology Overview :

  1. Python version 3.8.1
  2. Django 3.1

## Front-end Part

  1. * HTML
  2. * CSS
  3. * Bootstrap
  4. * JavaScript

## Back-end

  1. * Django
  2. * SQLite 3

Download Link

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Fake Product Review Detection using Machine Learning

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Latest Machine Learning Project with Source Code

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Online reviews play a very important role for decision-making in today's e-commerce. Large parts of the population, i.e. customers read product or store reviews before deciding what to buy or where to buy and whether to buy or not. Because writing fake / fraudulent reviews comes with monetary gain, online review websites there has been a huge increase in tricky opinion spam. Basically, an untruthful review is a fake review or fraudulent review or opinion spam. Positive reviews of a target object can attract more customers and increase sales; negative reviews of a target object can result in lower demand and lower sales. Fake review detection has attracted considerable attention in recent years. Most review sites, however, still do not filter fake reviews publicly. Yelp is an exception that over the past few years

has filtered reviews. Yelp's algorithm, however, is a business secret. In this work, by analyzing their filtered reviews, we try to find out what Yelp could do. The results will be useful in their filtering effort for other review hosting sites. Filtering has two main approaches: supervised and unmonitored learning. There are also about two types in terms of the characteristics used: linguistic characteristics and behavioral characteristics. Through supervised learning approach we have tried to make a model which can identify the fake review with almost 70 percent accuracy.

As the Internet continues to grow in size and importance, the quantity and impact of online reviews is increasing continuously. Reviews can influence people across a wide range of industries, but they are particularly important in e-commerce, where comments and reviews on products and services are often the most convenient, if not the only, way for a buyer to decide whether to buy them.

Model training

Refer to the Jupyter notebooks in research folder to know the steps taken for preprocessing, model development and algorithms used. Although we experemented with different models, we found Naive Bayes to be most accurate with F1 score of 77%.

Installing and running this app:

  1. Requirements: Use pip install/conda install to download following packages
  2. Numpy, pandas
  3. sklearn
  4. spacy
  5. Django 2.1
  6. pickle
  7. tqdm
  8. running the app:

Installation Step :- 

  1. Go to folder containing and run command: python runserver
  2. Once the server starts, open browser. The app runs on
  3. fake_reviews.txt and real_reviews.txt contains some reviews that can be used to test the working of model.