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.

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Who I Am

Navya Chennawar

I'm a first-year Data Science student with a deep curiosity for technology and a passion for data-driven problem-solving. I believe in the power of AI and machine learning to create meaningful impact in underserved communities.

I actively build my skills by diving into new programming languages, experimenting with tools like Python and TensorFlow, and taking on practical projects that push me beyond my comfort zone. Collaboration is at the heart of how I learn — I thrive in teams where ideas flow freely.

I'm looking to get hands-on experience in building real-world tech solutions. My goal is to combine technical skill with social awareness to develop solutions for challenges that truly matter.

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