Beautiful Python Code

Flight price prediction is a challenging task that involves analyzing various factors that affect the ticket prices, such as date, time, destination, airline, seasonality, demand and supply. You can use Python to build a machine learning model that predicts the flight prices based on historical data and features. Here are some examples of flight price prediction using Python: Using Kaggle dataset of flight booking data obtained from “Ease My Trip” website to perform exploratory data analysis and build a regression model using scikit-learn library. # Import libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.linear_model import LinearRegression # Read the dataset data = pd.read_csv("flight_price_prediction.csv") # Explore the dataset data.head() # Plot a boxplot of price vs airline sns.boxplot(x="Airline", y="Price", data=data) plt.xticks(rotation=90) plt.show() # Train a linear regression m...

Stock Market Predictions with LSTM in Python is a tutorial that shows how to create a three-layer LSTM model using TensorFlow and Keras. Here is a snippet of how to define the model: # Define the LSTM model model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], 1))) model.add(LSTM(units=50)) model.add(Dense(1))   # Compile and fit the model model.compile(loss='mean_squared_error', optimizer='adam') model.fit(x_train, y_train, epochs=25, batch_size=32) Stock Market Analysis + Prediction using LSTM is a notebook that shows how to load stock market data from Yahoo finance using yfinance module, how to explore and visualize time-series data using pandas, matplotlib, and seaborn, how to measure the correlation between stocks, how to measure the risk of investing in a particular stock, and how to use LSTM (Long Short-Term Memory) model for predicting future stock prices. Here is a snippet of how to create and train an LSTM model f...

XRP is a cryptocurrency that powers the Ripple network, which is a system for cross-border payments. Predicting XRP price can be done using Python and machine learning techniques, such as regression or neural networks. Here is an example of a code snippet that uses a neural network to predict XRP price based on historical data: # Import libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, LSTM # Load data df = pd.read_csv('XRP-USD.csv') df = df.dropna() df = df[['Close']] # Scale data scaler = MinMaxScaler(feature_range=(0,1)) df = scaler.fit_transform(df) # Split data into train and test sets train_size = int(len(df) * 0.8) test_size = len(df) - train_size train_data, test_data = df[0:train_size,:], df[train_size:len(df),:] # Create x_train and y_train data sets x_train = [] y_train = [] for i in range(6...

Cryptocurrency price prediction is the task of forecasting the future prices of a digital currency based on historical data and various factors. There are various methods and libraries in Python that can perform this task, such as machine learning, time series analysis, AutoTS, etc. One example is using a machine learning algorithm to predict the future prices of Dogecoin, a popular cryptocurrency . Here is a code snippet that shows how to use it: # Importing libraries import pandas as pd import numpy as np from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split # Reading data from csv file data = pd.read_csv("DOGE-USD.csv") # Selecting features and target X = data[["Open", "High", "Low", "Volume"]] y = data["Close"] # Splitting data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # Creating and fitting a linear regression model m...

Data analysis on Instagram post reach is a useful way to understand how your posts perform on the social media platform and what factors influence their engagement. You can use Python to scrape, analyze and visualize Instagram data using various libraries and tools. Here are some examples of data analysis on Instagram post reach using Python: Using pandas, numpy, matplotlib, seaborn and plotly.express libraries to analyze the reach of an Instagram account based on followers count, hashtags used, caption length and time of posting. # Import libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import plotly.express as px # Read the dataset data = pd.read_csv("instagram_reach.csv") # Explore the dataset data.head()       # Plot the correlation matrix sns.heatmap(data.corr(), annot=True) plt.show() # Plot a scatter plot of followers vs likes px.scatter(data, x="Followers", y="Likes") Using sklearn librar...