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Medical Insurance Price Prediction using Machine Learning - Python

Stock Price Prediction Project using TensorFlow

Last Updated : 08 Apr, 2025

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Stock price prediction is a challenging task in the field of finance with applications ranging from personal investment strategies to algorithmic trading. In this article we will explore how to build a stock price prediction model using TensorFlow and Long Short-Term Memory (LSTM) networks a type of recurrent neural network (RNN) which is well-suited for Timeseries data like stock prices.

Below is the step by step implementation:

1. Importing Libraries

In this article we will import Pandas, Numpy, Matplotlib, Seaborn and TensorFlow.

Python

import pandas as pd import matplotlib.pyplot as plt import numpy as np import tensorflow as tf from tensorflow import keras import seaborn as sns import os from datetime import datetime  import warnings warnings.filterwarnings("ignore")

2. Loading the Dataset

We will load the dataset containing stock prices over a 5-year period. The read_csv function loads the dataset into a pandas DataFrame for further analysis. You can download dataset from here.

delimiter=',': Specifies that commas separate values in the CSV file.
on_bad_lines='skip': Skips any malformed lines in the CSV file.

Python

data = pd.read_csv('all_stocks_5yr.csv', delimiter=',', on_bad_lines='skip') print(data.shape) print(data.sample(7))

Output:

Since the given data consists of a date feature, this is more likely to be an 'object' data type.

Python

data.info()

Output:

Stock Price Prediction Project using TensorFlow

Whenever we deal with the date or time feature, it should always be in the DateTime data type. Pandas library helps us convert the object date feature to the DateTime data type.

Python

data['date'] = pd.to_datetime(data['date']) data.info()

Stock Price Prediction Project using TensorFlow

3. Exploratory Data Analysis

Exploratory Data Analysis is a technique that is used to analyze the data through visualization and manipulation. For this project let us visualize the data of famous companies such as Nvidia, Google, Apple, Facebook and so on. First let us consider a few companies and visualize the distribution of open and closed Stock prices through 5 years.

Python

companies = ['AAPL', 'AMD', 'FB', 'GOOGL', 'AMZN', 'NVDA', 'EBAY', 'CSCO', 'IBM']  plt.figure(figsize=(15, 8)) for index, company in enumerate(companies, 1):     plt.subplot(3, 3, index)     c = data[data['Name'] == company]     plt.plot(c['date'], c['close'], c="r", label="close", marker="+")     plt.plot(c['date'], c['open'], c="g", label="open", marker="^")     plt.title(company)     plt.legend()     plt.tight_layout()  plt.figure(figsize=(15, 8)) for index, company in enumerate(companies, 1):     plt.subplot(3, 3, index)     c = data[data['Name'] == company]     plt.plot(c['date'], c['volume'], c='purple', marker='*')     plt.title(f"{company} Volume")     plt.tight_layout()

Output:

Analyzing Close and Open prices for stocks of 9 different country

Now let's plot the volume of trade for these 9 stocks as well as a function of time.

Python

plt.figure(figsize=(15, 8)) for index, company in enumerate(companies, 1):     plt.subplot(3, 3, index)     c = data[data['Name'] == company]     plt.plot(c['date'], c['volume'], c='purple', marker='*')     plt.title(f"{company} Volume")     plt.tight_layout()

Output:

Analyzing volume for stocks of 9 different country

Now let's analyze the data for Apple Stocks from 2013 to 2018.

Python

apple = data[data['Name'] == 'AAPL'] prediction_range = apple.loc[(apple['date'] > datetime(2013,1,1))  & (apple['date']<datetime(2018,1,1))] plt.plot(apple['date'],apple['close']) plt.xlabel("Date") plt.ylabel("Close") plt.title("Apple Stock Prices") plt.show()

Output:

The overall trend in the prices of the Apple Stocks

Now let's select a subset of the whole data as the training data so that we will be left with a subset of the data for the validation part as well.

Python

close_data = apple.filter(['close']) dataset = close_data.values training = int(np.ceil(len(dataset) * .95)) print(training)

Output:

1197

Now we have the training data length, next applying scaling and preparing features and labels that are x_train and y_train.

Python

from sklearn.preprocessing import MinMaxScaler  scaler = MinMaxScaler(feature_range=(0, 1)) scaled_data = scaler.fit_transform(dataset)  train_data = scaled_data[0:int(training), :] # prepare feature and labels x_train = [] y_train = []  for i in range(60, len(train_data)):     x_train.append(train_data[i-60:i, 0])     y_train.append(train_data[i, 0])  x_train, y_train = np.array(x_train), np.array(y_train) x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))

4. Build LSTM network using TensorFlow

Using TensorFlow, we can easily create LSTM models. It is used in Recurrent Neural Networks for sequence models and time series data. It is used to avoid the vanishing gradient issue which is widely occurred in training RNN. To stack multiple LSTM in TensorFlow it is mandatory to use return_sequences = True. Since our data is time series varying we apply no activation to the output layer and it remains as 1 node.

Python

model = keras.models.Sequential() model.add(keras.layers.LSTM(units=64,                             return_sequences=True,                             input_shape=(x_train.shape[1], 1))) model.add(keras.layers.LSTM(units=64)) model.add(keras.layers.Dense(32)) model.add(keras.layers.Dropout(0.5)) model.add(keras.layers.Dense(1)) model.summary

Output:

Model summary to analyze the architecture of the model

5. Model Compilation and Training

While compiling a model we provide these three essential parameters:

optimizer – This is the method that helps to optimize the cost function by using gradient descent.
loss – The loss function by which we monitor whether the model is improving with training or not.
metrics – This helps to evaluate the model by predicting the training and the validation data.

Python

model.compile(optimizer='adam',               loss='mean_squared_error') history = model.fit(x_train,                     y_train,                     epochs=10)

Output:

Progress of model training epoch by epoch

For predicting we require testing data, so we first create the testing data and then proceed with the model prediction.

Python

test_data = scaled_data[training - 60:, :] x_test = [] y_test = dataset[training:, :] for i in range(60, len(test_data)):     x_test.append(test_data[i-60:i, 0])  x_test = np.array(x_test) x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))  predictions = model.predict(x_test) predictions = scaler.inverse_transform(predictions)  mse = np.mean(((predictions - y_test) ** 2)) rmse = np.sqrt(mse)  print("MSE", mse) print("RMSE", np.sqrt(mse))

Output:

Screenshot-2025-04-08-135906 — Model Accuracy

Now that we have predicted the testing data, let us visualize the final results.

Python

train = apple[:training] test = apple[training:] test['Predictions'] = predictions  plt.figure(figsize=(10, 8)) plt.plot(train['date'], train['close']) plt.plot(test['date'], test[['close', 'Predictions']]) plt.title('Apple Stock Close Price') plt.xlabel('Date') plt.ylabel("Close") plt.legend(['Train', 'Test', 'Predictions'])

Output:

Prediction for Stock Prices of Apple

The chart shows Apple’s stock closing price over time with the "Train" data representing historical prices used for model training, "Test" data for evaluation and "Predictions" showing the model’s forecasted values. It visually demonstrates how well the model’s predictions align with actual stock prices highlighting areas of accurate forecasting and divergence.

You can download the source code from here: click here.

Medical Insurance Price Prediction using Machine Learning - Python

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Speech Recognition in Python using Google Speech API

Speech recognition means converting spoken words into text. It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, youâ€™ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require

Voice Assistant using python

Speech recognition is the process of turning spoken words into text. It is a key part of any voice assistant. In Python the SpeechRecognition module helps us do this by capturing audio and converting it to text. In this guide weâ€™ll create a basic voice assistant using Python.Step 1: Install Required

Human Activity Recognition - Using Deep Learning Model

Human activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque

Fine-tuning BERT model for Sentiment Analysis

Google created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train

Sentiment Classification Using BERT

BERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec

Sentiment Analysis with an Recurrent Neural Networks (RNN)

Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews

Building an Autocorrector Using NLP in Python

Autocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N

Python | NLP analysis of Restaurant reviews

Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of mach

Restaurant Review Analysis Using NLP and SQLite

Normally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei

Twitter Sentiment Analysis using Python

This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. Itâ€™s also known as opinion mini

Clustering Projects

Customer Segmentation using Unsupervised Machine Learning in Python

Customer Segmentation involves grouping customers based on shared characteristics, behaviors and preferences. By segmenting customers, businesses can tailor their strategies and target specific groups more effectively and enhance overall market value. Today we will use Unsupervised Machine Learning

Music Recommendation System Using Machine Learning

When did we see a video on youtube let's say it was funny then the next time you open your youtube app you get recommendations of some funny videos in your feed ever thought about how? This is nothing but an application of Machine Learning using which recommender systems are built to provide persona

K means Clustering â€“ Introduction

K-Means Clustering is an Unsupervised Machine Learning algorithm which groups unlabeled dataset into different clusters. It is used to organize data into groups based on their similarity. Understanding K-means ClusteringFor example online store uses K-Means to group customers based on purchase frequ

Image Segmentation using K Means Clustering

Image segmentation is a technique in computer vision that divides an image into different segments. This can help identify specific objects, boundaries or patterns in the image.Â Image is basically a set of given pixels and in image segmentation pixels with similar intensity are grouped together. Im

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