import numpy as np
def predict(x):
  return model.fit(x)
SELECT * FROM data
if accuracy > 0.9:
  deploy(model)
torch.nn.Linear
sklearn.ensemble
pd.DataFrame()
plt.show()
loss.backward()
optimizer.step()
epochs = 100
data.dropna()
X_train, X_test
cross_val_score
import tensorflow
model.compile()
batch_size = 32
learning_rate=1e-3
accuracy: 0.976
precision: 0.981
import numpy as np
def predict(x):
  return model.fit(x)
SELECT * FROM data
if accuracy > 0.9:
  deploy(model)
torch.nn.Linear
sklearn.ensemble
pd.DataFrame()
plt.show()
loss.backward()
optimizer.step()
epochs = 100
data.dropna()
X_train, X_test
cross_val_score
import tensorflow
model.compile()
batch_size = 32
learning_rate=1e-3
accuracy: 0.976
precision: 0.981
import numpy as np
def predict(x):
  return model.fit(x)
SELECT * FROM data
if accuracy > 0.9:
  deploy(model)
torch.nn.Linear
sklearn.ensemble
pd.DataFrame()
plt.show()
loss.backward()
optimizer.step()
epochs = 100
data.dropna()
X_train, X_test
cross_val_score
import tensorflow
model.compile()
batch_size = 32
learning_rate=1e-3
accuracy: 0.976
precision: 0.981
import numpy as np
def predict(x):
  return model.fit(x)
SELECT * FROM data
if accuracy > 0.9:
  deploy(model)
torch.nn.Linear
sklearn.ensemble
pd.DataFrame()
plt.show()
loss.backward()
optimizer.step()
epochs = 100
data.dropna()
X_train, X_test
cross_val_score
import tensorflow
model.compile()
batch_size = 32
learning_rate=1e-3
accuracy: 0.976
precision: 0.981
import numpy as np
def predict(x):
  return model.fit(x)
SELECT * FROM data
if accuracy > 0.9:
  deploy(model)
torch.nn.Linear
sklearn.ensemble
pd.DataFrame()
plt.show()
loss.backward()
optimizer.step()
epochs = 100
data.dropna()
X_train, X_test
cross_val_score
import tensorflow
model.compile()
batch_size = 32
learning_rate=1e-3
accuracy: 0.976
precision: 0.981
import numpy as np
def predict(x):
  return model.fit(x)
SELECT * FROM data
if accuracy > 0.9:
  deploy(model)
torch.nn.Linear
sklearn.ensemble
pd.DataFrame()
plt.show()

import numpy as np
def predict(x):
  return model.fit(x)
SELECT * FROM data
if accuracy > 0.9:
  deploy(model)
torch.nn.Linear
sklearn.ensemble
pd.DataFrame()
plt.show()
loss.backward()
optimizer.step()
epochs = 100
data.dropna()
X_train, X_test
cross_val_score
import tensorflow
model.compile()
batch_size = 32
learning_rate=1e-3
accuracy: 0.976
precision: 0.981
import numpy as np
def predict(x):
  return model.fit(x)
SELECT * FROM data
if accuracy > 0.9:
  deploy(model)
torch.nn.Linear
sklearn.ensemble
pd.DataFrame()
plt.show()
loss.backward()
optimizer.step()
epochs = 100
data.dropna()
X_train, X_test
cross_val_score
import tensorflow
model.compile()
batch_size = 32
learning_rate=1e-3
accuracy: 0.976
precision: 0.981
import numpy as np
def predict(x):
  return model.fit(x)
SELECT * FROM data
if accuracy > 0.9:
  deploy(model)
torch.nn.Linear
sklearn.ensemble
pd.DataFrame()
plt.show()
loss.backward()
optimizer.step()
epochs = 100
data.dropna()
X_train, X_test
cross_val_score
import tensorflow
model.compile()
batch_size = 32
learning_rate=1e-3
accuracy: 0.976
precision: 0.981
import numpy as np
def predict(x):
  return model.fit(x)
SELECT * FROM data
if accuracy > 0.9:
  deploy(model)
torch.nn.Linear
sklearn.ensemble
pd.DataFrame()
plt.show()
loss.backward()
optimizer.step()
epochs = 100
data.dropna()
X_train, X_test
cross_val_score
import tensorflow
model.compile()
batch_size = 32
learning_rate=1e-3
accuracy: 0.976
precision: 0.981
import numpy as np
def predict(x):
  return model.fit(x)
SELECT * FROM data
if accuracy > 0.9:
  deploy(model)
torch.nn.Linear
sklearn.ensemble
pd.DataFrame()
plt.show()
loss.backward()
optimizer.step()
epochs = 100
data.dropna()
X_train, X_test
cross_val_score
import tensorflow
model.compile()
batch_size = 32
learning_rate=1e-3
accuracy: 0.976
precision: 0.981
import numpy as np
def predict(x):
  return model.fit(x)
SELECT * FROM data
if accuracy > 0.9:
  deploy(model)
torch.nn.Linear
sklearn.ensemble
pd.DataFrame()
plt.show()

Hey, I'm

NAVYA CHENNAWAR

AI & Data Science Enthusiast

Driven by curiosity and committed to building technology
that bridges gaps and solves real-world problems.

💼 LinkedIn 💻 GitHub 📧 Email Me

↓ Scroll to Explore

Explore My World

Click a circle — or press 1–7 — to dive in

Who I Am

Projects

Capabilities

Strengths

Learning

Experience

Contact

Who I Am

Navya Chennawar

I’m a Data Science student passionate about technology, data, and problem-solving. I enjoy learning new programming languages and tools, working on projects, and collaborating with peers to gain practical experience.

Currently, I am focused on strengthening my coding skills and improving my technical knowledge, and I am eager to apply what I learn to real-world challenges.

Get in Touch

Have a project idea, collaboration proposal, or just want to chat about AI? Drop me an email — I'd love to connect!

📬 Send Me an Email

navyach2219@gmail.com