Machine learning frameworks like TensorFlow, PyTorch, or scikit-learn, along with libraries for data manipulation and visualization such as Pandas and Matplotlib we use in generating AI projects. From matlabsimulation.com PhD and MS scholars can get a lot of ideas for projects involving artificial intelligence that is written in Python. Python technique are well-known, here we discuss several ideas for projects involving artificial intelligence in Python.
Here are some of the project ideas with a brief outline of how one could approach them with Python code:
Sentiment Analysis
Natural Language Toolkit (nltk), scikit-learn can be used to categorize the sentiment of a given text as positive, negative, or neutral. A sample of the code work is given.
python
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
# Your text data and labels
text_data = [“I love this!”, “I hate this!”, “It’s okay.”]
labels = [1, 0, 2]
# Feature extraction
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(text_data)
# Split data
X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2)
# Model training
clf = MultinomialNB()
clf.fit(X_train, y_train)
# Prediction
y_pred = clf.predict(X_test)
Object Detection
We can detect and classify objects within images by using TensorFlow, OpenCV.
python
import tensorflow as tf
import cv2
# Load a pre-trained model
model = tf.saved_model.load(“ssd_mobilenet_v2_coco/saved_model”)
# Read image
image = cv2.imread(‘image.jpg’)
# Perform inference
# …
Chatbot
A simple conversational agent is built here the libraries needed are ChatterBot aa example of code is shown.
python
from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer
chatbot = ChatBot(‘MyBot’)
trainer = ListTrainer(chatbot)
# Train the chatbot
trainer.train([
‘Hi’,
‘Hello’,
‘How are you?’,
‘I am fine.’
])
# Get a response
response = chatbot.get_response(‘Hello’)
Stock Price Prediction
We can predict future stock prices based on historical data by using Pandas, scikit-learn libraries.
python
import pandas as pd
from sklearn.linear_model import LinearRegression
# Load data
data = pd.read_csv(‘stock_prices.csv’)
# Data preprocessing
# …
# Model training
model = LinearRegression()
model.fit(X_train, y_train)
# Prediction
y_pred = model.predict(X_test)
Image Classification
Here images can be classified into various categories by using TensorFlow, Keras libraries.
python
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten
# Create the model
model = Sequential()
# Add layers
model.add(Conv2D(64, kernel_size=3, activation=’relu’, input_shape=(28,28,1)))
model.add(Conv2D(32, kernel_size=3, activation=’relu’))
model.add(Flatten())
model.add(Dense(10, activation=’softmax’))
# Compile the model
model.compile(optimizer=’adam’, loss=’categorical_crossentropy’, metrics=[‘accuracy’])
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=3)
The above presented are just some reference coding, our research team add more features, by optimizing the model or mixing them into algorithms, techniques,functions and frame out effectively. While we add additional preprocessing, error handling, and fine-tuning for its better outcome.
Python Artificial intelligence project Ideas
Python Artificial intelligence project Ideas are shared by our leading development team. Novel ideas with new tools and technologies will be assisted for scholar’s python project.
Here are a few projects on artificial intelligence in the field python, the most exciting artificial intelligence project ideas are discussed below…
- Voice Analysis Framework for Asthma-COVID-19 Early Diagnosis and Prediction: AI-based Mobile Cloud Computing Application
- AI-Based Online P2P Lending Risk Assessment on Social Network Data with Missing Value
- The Role of AI Chatbots in Mental Health Related Public Services in a (Post)Pandemic World: A Review and Future Research Agenda
- Cyber Security and Securing Subjective Patient Quality Engagements in Medical Applications: AI and Vulnerabilities
- An AI based solution for the control of 3D real-time sensor-based gaming
- Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era
- Robust AI-enabled Simulation of Treatment Paths with Markov Decision Process for Breast Cancer Patients
- Revitalizing Traditional Health Practices with Healing Hands: An AI powered Chatbot
- Analysis And Implementation of a Novel AI-Based Hybrid Model for Detecting, Predicting and Identification Of COVID-19 Spread
- A study on the applicability of AI in Pharmaceutical Industry
- A Comprehensive Review of the Negative Impact of Integration of AI in Social-Media in Mental Health of Users
- Traffic Safety in Future Cities by Using a Safety Approach Based on AI and Wireless Communications
- A Deep Reinforcement Learning Agent for General Video Game AI Framework Games
- Analytical study on use of AI techniques in tourism sector for smarter customer experience management
- Artificial intelligence techniques: an introduction to their use for modelling environmental systems
- Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
- Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery
- The impact of artificial intelligence in medicine on the future role of the physician
- A survey on explainable artificial intelligence (xai): Toward medical xai