Skip to content

Instantly share code, notes, and snippets.

View MgeeeeK's full-sized avatar
:shipit:
Finding Bugs

Mragank Shekhar MgeeeeK

:shipit:
Finding Bugs
12 followers · 12 following
View GitHub Profile
@MgeeeeK
MgeeeeK / gsoc2020_mragank_report.md
Last active August 31, 2020 13:23
GSOC 2020 project report: Raster Awareness in PySAL
@MgeeeeK
MgeeeeK / nn.py
Created October 13, 2019 19:21
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
model = Sequential()
model.add(Dense(32, input_dim=5, activation="relu"))
model.add(Dense(32, activation="relu"))
model.add(Dense(2, activation="sigmoid"))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy',precision_m, recall_m])
@MgeeeeK
MgeeeeK / rf.py
Created October 13, 2019 19:05
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=100, criterion = "gini")
clf = clf.fit(train_x, train_y)
pred_y = clf.predict(test_x)
@MgeeeeK
MgeeeeK / pc.py
Created October 13, 2019 18:54
from sklearn.linear_model import Perceptron
clf = Perceptron(tol=1e-3)
clf = clf.fit(train_x, train_y)
pred_y = clf.predict(test_x)
@MgeeeeK
MgeeeeK / lr.py
Created October 13, 2019 18:14
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(solver='lbfgs')
clf = clf.fit(train_x, train_y)
pred_y = clf.predict(test_x)
@MgeeeeK
MgeeeeK / cart.py
Created October 13, 2019 18:09
from sklearn import tree
clf = tree.DecisionTreeClassifier()
clf = clf.fit(train_x, train_y)
pred_y = clf.predict(test_x)
@MgeeeeK
MgeeeeK / split.py
Created October 13, 2019 17:58
from sklearn.model_selection import train_test_split
from sklearn import metrics
train_x, test_x, train_y, test_y = train_test_split( X.values, Y.values, test_size=0.3)
@MgeeeeK
MgeeeeK / normalize.py
Created October 13, 2019 17:51
norm
x = X.values
mmscaler = preprocessing.MinMaxScaler()
x_norm = mmscaler.fit_transform(x)
X = pd.DataFrame(x_norm)
print(X.head())
@MgeeeeK
MgeeeeK / label_encoder.py
Created October 13, 2019 17:42
encoding
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
X['Delivery Number']= le.fit_transform(X['Delivery Number'])
X['Delivery Time']= le.fit_transform(X['Delivery Time'])
X['Blood Pressure']= le.fit_transform(X['Blood Pressure'])
X['Heart Problem']= le.fit_transform(X['Heart Problem'])
Y['Caesarian']= le.fit_transform(Y['Caesarian'])
@MgeeeeK
MgeeeeK / X_Y.py
Created October 13, 2019 17:32
X = df.iloc[:,:5]
Y = df.iloc[:,-1:]
print(X.head(),"\n\n",Y.head())