markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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Check how to create it:* pd.DataFrame().from_records()* pd.DataFrame().from_dict() | pd.DataFrame.from_records(some_data)
pd.DataFrame.from_dict() | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
This data set is too big for github, download it from [here](https://www.kaggle.com/START-UMD/gtd). You will need to register on Kaggle first. | df = pd.read_csv('globalterrorismdb_0718dist.csv', encoding='ISO-8859-1') | /opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3145: DtypeWarning: Columns (4,6,31,33,61,62,63,76,79,90,92,94,96,114,115,121) have mixed types.Specify dtype option on import or set low_memory=False.
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
| MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
Let's explore the second set of data. How many rows and columns are there? | df.shape | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
General information on this data set: | df.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 181691 entries, 0 to 181690
Columns: 135 entries, eventid to related
dtypes: float64(55), int64(22), object(58)
memory usage: 187.1+ MB
| MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
Let's take a look at the dataset information. In .info (), you can pass additional parameters, including:* **verbose**: whether to print information about the DataFrame in full (if the table is very large, then some information may be lost);* **memory_usage**: whether to print memory consumption (the default is True, b... | df.describe()
df.describe(include=['object', 'int']) | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
The describe method shows the basic statistical characteristics of the data for each numeric feature (int64 and float64 types): the number of non-missing values, mean, standard deviation, range, median, 0.25 and 0.75 quartiles. How to look only at the column names, index: | df.columns
df.index | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
How to look at the first 10 lines? | df.head(10) | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
How to look at the last 15 lines? | df.tail(15) | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
How to request only one particular line (by counting lines)? | df.head(4)
#the first 3 lines
df.iloc[:3] # the number of rows by counting them | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
How to request only one particular line by its index? | # the first lines till the row with the index 3
df.loc[:3] # 3 is treated as an index | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
Look only at the unique values of some columns. | list(df['city'].unique()) | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
How many unique values there are in ```city``` column? = On how many cities this data set hold information on terrorist attacks? | df['city'].nunique() | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
In what years did the largest number of terrorist attacks occur (according to only to this data set)? | df['iyear'].value_counts().head(5)
df['iyear'].value_counts()[:5] | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
How we can sort all data by year in descending order? | df['iyear'].sort_values()
df.sort_values(by='iyear', ascending=False) | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
Which data types we have in each column? | dict(df.dtypes) | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
How to check the missing values? | df
df.isna()
dict(df.isna().sum())
df.dropna(axis=1)
df.head(5)
df['attacktype2'].min()
df['attacktype2'].max()
df['attacktype2'].mode()
df['attacktype2'].median()
df['attacktype2'].mean()
df['attacktype2'].fillna(df['attacktype2'].mode()) | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
Let's delete a column ```approxdate``` from this data set, because it contains a lot of missing values: | df.drop(['approxdate'], axis=1, inplace=True) | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
Create a new variable ```casualties``` by summing up the value in ```Killed``` and ```Wounded```. | set(df.columns)
df['casualties'] = df['nwound'] + df['nkill']
df.head() | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
Rename a column ```iyear``` to ```Year```: | df.rename({'iyear' : 'Year'}, axis='columns', inplace=True)
df | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
How to drop all missing values? Replace these missing values with others? | df.dropna(inplace=True) | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
**Task!** Use a function to replace NaNs (=missing values) to a string 'None' in ```related``` column | # TODO | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
For the selected columns show its mean, median (and/or mode). | df['Year'].mean() | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
Min, max and sum: | df['Year'].sum()
sum(df['Year'])
max('word') | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
Filter the dataset to look only at the attacks after 2015 year | df[df.Year > 2015] | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
What if we have several conditions? Try it out | df[(df.Year > 2015) & (df.extended == 1)] | _____no_output_____ | MIT | week8/in_class_notebooks/week8-192.ipynb | anamarina/Data_Analysis_in_Python |
Tips Introduction:This exercise was created based on the tutorial and documentation from [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/index.html) The dataset being used is tips from Seaborn. Step 1. Import the necessary libraries: | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns | _____no_output_____ | BSD-3-Clause | 07_Visualization/Tips/Exercises.ipynb | alexkataev/pandas_exercises |
Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Tips/tips.csv). Step 3. Assign it to a variable called tips | url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Tips/tips.csv'
tips = pd.read_csv(url, index_col=0)
tips.reset_index()
tips | _____no_output_____ | BSD-3-Clause | 07_Visualization/Tips/Exercises.ipynb | alexkataev/pandas_exercises |
Step 4. Delete the Unnamed 0 column | # already done | _____no_output_____ | BSD-3-Clause | 07_Visualization/Tips/Exercises.ipynb | alexkataev/pandas_exercises |
Step 5. Plot the total_bill column histogram | sns.set(style='white')
sns.set_context(rc = {'patch.linewidth': 2.0})
ax = sns.histplot(tips['total_bill'], kde=True, stat='density')
ax.set(xlabel='Value', ylabel='Frequency')
ax.set_title('Total Bill', size=15)
sns.despine();
# Original solution:
# create histogram
ttbill = sns.distplot(tips.total_bill);
# set la... | /anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
war... | BSD-3-Clause | 07_Visualization/Tips/Exercises.ipynb | alexkataev/pandas_exercises |
Step 6. Create a scatter plot presenting the relationship between total_bill and tip | sns.jointplot(x=tips['total_bill'], y=tips['tip'], xlim=(0, 60), ylim=(0, 12)); | _____no_output_____ | BSD-3-Clause | 07_Visualization/Tips/Exercises.ipynb | alexkataev/pandas_exercises |
Step 7. Create one image with the relationship of total_bill, tip and size. Hint: It is just one function. | sns.pairplot(data=tips[['total_bill', 'tip', 'size']]);
# Original solution:
#
# sns.pairplot(tips) | _____no_output_____ | BSD-3-Clause | 07_Visualization/Tips/Exercises.ipynb | alexkataev/pandas_exercises |
Step 8. Present the relationship between days and total_bill value | sns.set_style('whitegrid')
plt.figure(figsize=(8, 6))
ax = sns.stripplot(x=tips['day'], y=tips['total_bill'])
ax.set_ylim(0, 60);
# Original solution:
#
# sns.stripplot(x = "day", y = "total_bill", data = tips, jitter = True);
# What a "jitter" is (for demonstration purposes):
sns.stripplot(x = "day", y = "total_bill"... | _____no_output_____ | BSD-3-Clause | 07_Visualization/Tips/Exercises.ipynb | alexkataev/pandas_exercises |
Step 9. Create a scatter plot with the day as the y-axis and tip as the x-axis, differ the dots by sex | sns.set_style("whitegrid")
plt.figure(figsize=(8, 6))
ax = sns.scatterplot(data=tips, x='tip', y='day', hue='sex');
ax.yaxis.grid(False)
ax.legend(title='Sex', framealpha = 1, edgecolor='w');
# Original solution:
#
# sns.stripplot(x = "tip", y = "day", hue = "sex", data = tips, jitter = True); | _____no_output_____ | BSD-3-Clause | 07_Visualization/Tips/Exercises.ipynb | alexkataev/pandas_exercises |
Step 10. Create a box plot presenting the total_bill per day differetiation the time (Dinner or Lunch) | plt.figure(figsize=(12, 6))
sns.boxplot(data=tips, x='day', y='total_bill', hue='time'); | _____no_output_____ | BSD-3-Clause | 07_Visualization/Tips/Exercises.ipynb | alexkataev/pandas_exercises |
Step 11. Create two histograms of the tip value based for Dinner and Lunch. They must be side by side. | sns.set_style('ticks')
g = sns.FacetGrid(data=tips, col='time')
g.map(sns.histplot, 'tip', bins=10)
g.set(xlim=(0, 12), ylim=(0, 60), xticks=range(0, 13, 2), yticks=range(0, 61, 10));
sns.despine();
# Original solution:
#
# # better seaborn style
# sns.set(style = "ticks")
# # creates FacetGrid
# g = sns.FacetGrid(tip... | _____no_output_____ | BSD-3-Clause | 07_Visualization/Tips/Exercises.ipynb | alexkataev/pandas_exercises |
Step 12. Create two scatterplots graphs, one for Male and another for Female, presenting the total_bill value and tip relationship, differing by smoker or no smoker They must be side by side. | g = sns.FacetGrid(data=tips, col='sex')
g.map_dataframe(sns.scatterplot, x='total_bill', y='tip', hue='smoker')
g.add_legend(title='Smoker')
g.set_axis_labels('Total bill', 'Tip')
g.set(xlim=(0, 60), ylim=(0, 12), xticks=range(0, 61, 10), yticks=range(0, 13, 2));
# Original solution:
#
# g = sns.FacetGrid(tips, col =... | _____no_output_____ | BSD-3-Clause | 07_Visualization/Tips/Exercises.ipynb | alexkataev/pandas_exercises |
BONUS: Create your own question and answer it using a graph. | g = sns.FacetGrid(data=tips, col='sex')
g.map(sns.kdeplot, 'total_bill');
sns.kdeplot(tips['total_bill'], hue=tips['sex']);
sns.histplot(data=tips, x='total_bill', hue='sex');
tips.groupby('sex')[['total_bill']].sum()
tips.groupby('sex')[['total_bill']].count()
males = tips[tips['sex'] == 'Male'].sample(87)
males.head(... | _____no_output_____ | BSD-3-Clause | 07_Visualization/Tips/Exercises.ipynb | alexkataev/pandas_exercises |
Model_2_Linear_Regression | # Update sklearn to prevent version mismatches
!pip install sklearn --upgrade
import pandas as pd | _____no_output_____ | ADSL | starter_code/.ipynb_checkpoints/Model_2_Logistic_Regression-checkpoint.ipynb | bandipara/machine-learning-challenge |
Read the CSV and Perform Basic Data Cleaning | df = pd.read_csv("exoplanet_data.csv")
# Drop the null columns where all values are null
df = df.dropna(axis='columns', how='all')
# Drop the null rows
df = df.dropna()
df.head() | _____no_output_____ | ADSL | starter_code/.ipynb_checkpoints/Model_2_Logistic_Regression-checkpoint.ipynb | bandipara/machine-learning-challenge |
Create a Train Test Split | from sklearn.model_selection import train_test_split
y=df['koi_disposition']
X=df.drop(columns=['koi_disposition'])
X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=1, stratify=y)
X_train.head() | _____no_output_____ | ADSL | starter_code/.ipynb_checkpoints/Model_2_Logistic_Regression-checkpoint.ipynb | bandipara/machine-learning-challenge |
Preprocessing | from sklearn.preprocessing import MinMaxScaler
X_scaler = MinMaxScaler().fit(X_train)
X_train_scaled = X_scaler.transform(X_train)
X_test_scaled = X_scaler.transform(X_test) | _____no_output_____ | ADSL | starter_code/.ipynb_checkpoints/Model_2_Logistic_Regression-checkpoint.ipynb | bandipara/machine-learning-challenge |
Linear Regression | from sklearn.linear_model import LogisticRegression
model1 = LogisticRegression()
model1.fit(X_train_scaled, y_train)
print(f'Train = {model1.score(X_train_scaled, y_train)}')
print(f'Test = {model1.score(X_test_scaled, y_test)}') | Train = 0.8411214953271028
Test = 0.8409610983981693
| ADSL | starter_code/.ipynb_checkpoints/Model_2_Logistic_Regression-checkpoint.ipynb | bandipara/machine-learning-challenge |
GridSearch | # Create the GridSearchCV model
from sklearn.model_selection import GridSearchCV
param_grid= {'C':[1,5,10], 'penalty': ['l1','l2']}
grid1 = GridSearchCV(model1, param_grid)
grid1.fit(X_train_scaled, y_train)
print(grid1.best_params_)
print(grid1.best_score_) | {'C': 5, 'penalty': 'l1'}
0.8798397863818425
| ADSL | starter_code/.ipynb_checkpoints/Model_2_Logistic_Regression-checkpoint.ipynb | bandipara/machine-learning-challenge |
Save the model |
# save your model by updating "your_name" with your name
# and "your_model" with your model variable
# be sure to turn this in to BCS
# if joblib fails to import, try running the command to install in terminal/git-bash
import joblib
filename = 'Model_2_Linear_Regression'
joblib.dump(grid1, filename) | _____no_output_____ | ADSL | starter_code/.ipynb_checkpoints/Model_2_Logistic_Regression-checkpoint.ipynb | bandipara/machine-learning-challenge |
MatplotlibMatplotlib is a powerful tool for generating scientific charts of various sorts.This presentation only touches on some features of matplotlib. Please seehttps://jakevdp.github.io/PythonDataScienceHandbook/index.html or many otherresources for a moredetailed discussion,The following notebook shows how to use... | # Import numpy and matplotlib modules
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np
# Get x values between -2 and 2
xs = np.linspace(-2, 2, 21)
xs
# Compute array of f values for x values
fs = 0.2 * xs * xs + np.sin(xs + 1) - 0.5
fs
# Make a figure and plot x v... | _____no_output_____ | BSD-2-Clause | notebooks/matplotlib teaser.ipynb | flatironinstitute/mfa_jupyter_for_programming |
Solving an equationAt what value of $x$ in $[-2, 2]$ does $f(x) = 0$?Let's look at different plots for $f$ using functions to automate things. | def f(x):
return 0.2 * x ** 2 + np.sin(x + 1) - 0.5
def plot_f(low_x=-2, high_x=2, number_of_samples=30):
# Get an array of x values between low_x and high_x of length number_of_samples
xs = FILL_IN_THE_BLANK
fs = f(xs)
fig = plt.figure()
ax = plt.axes()
ax.plot(xs, fs);
plot_f()
plot_... | _____no_output_____ | BSD-2-Clause | notebooks/matplotlib teaser.ipynb | flatironinstitute/mfa_jupyter_for_programming |
Interactive plotsWe can make an interactive figure where we can try to locate the crossing point visually | from ipywidgets import interact
interact(plot_f, low_x=(-2.,2), high_x=(-2.,2))
# But we really should do it using an algorithm like binary search:
def find_x_at_zero(some_function, x_below_zero, x_above_zero, iteration_limit=10):
"""
Given f(x_below_zero)<=0 and f(x_above_zero) >= 0 iteratively use the
m... | _____no_output_____ | BSD-2-Clause | notebooks/matplotlib teaser.ipynb | flatironinstitute/mfa_jupyter_for_programming |
Baseline ModelWe build the baseline model according to our first hypothesis:Lower school attendance leeds to higher target. Import and Setup | import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
import matplotlib.pyplot as plt
%matplotlib inline
RSEED = 42
# imp... | _____no_output_____ | MIT | baseline_model.ipynb | JanaConradi/Zindi_Data_female_households_RSA |
`psa_00`: "Percentage listing present school attendance as: Yes" `target`: "Percentage of women head households with income under R19.6k out of total number of households" | sns.lmplot(y='target', x='psa_00', data=df, line_kws={'color': 'red'})
plt.title('Trend between school attendance and percentage of low income')
# define feature and target
X = df[["psa_00"]]
y = df.target | _____no_output_____ | MIT | baseline_model.ipynb | JanaConradi/Zindi_Data_female_households_RSA |
We have a Test and a Train dataset here in this notebook, but since the Test dataset doesn't have the target we will only use the Train dataset, und make our own test and train data from that. (The target was to predict and submit to the Zindi competition) | # train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=RSEED)
# Fit a basic linear regression model on the train data
lm = LinearRegression()
lm.fit(X_train, y_train)
# make predictions on test data
y_pred = lm.predict(X_test)
# evaluation metrics test
print(f"R2: {r... | R2: 0.6123813749418046
RMSE: 6.227259786482336
| MIT | baseline_model.ipynb | JanaConradi/Zindi_Data_female_households_RSA |
Leaf Rice Disease Detection using ResNet50V2 Architecture  | Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
| MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Importing Libraries | import keras
from keras import Sequential
from keras.applications import MobileNetV2
from keras.layers import Dense
from keras.preprocessing import image
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow.keras.utils import to_c... | _____no_output_____ | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Target Class | Class=[]
for file in os.listdir(directory):
Class+=[file]
print(Class)
print(len(Class)) | ['blast', 'blight', 'tungro']
3
| MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Mapping the Images | Map=[]
for i in range(len(Class)):
Map = Map+[i]
normal_mapping=dict(zip(Class,Map))
reverse_mapping=dict(zip(Map,Class))
def mapper(value):
return reverse_mapping[value]
set1=[]
set2=[]
count=0
for i in Class:
path=os.path.join(directory,i)
t=0
for image in os.listdir(path):
if imag... | _____no_output_____ | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Dividing Data and Test | data, dataa=zip(*set1)
test, test_test=zip(*set2)
label=to_categorical(dataa)
X=np.array(data)
y=np.array(label)
labell=to_categorical(test_test)
test=np.array(test)
labell=np.array(labell)
print(len(y))
print(len(labell)) | 180
60
| MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Train Test Split | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
print(X_train.shape,X_test.shape)
print(y_train.shape,y_test.shape) | (144, 100, 100, 3) (36, 100, 100, 3)
(144, 3) (36, 3)
| MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Image Generator | generator = ImageDataGenerator(horizontal_flip=True,vertical_flip=True,rotation_range=20,zoom_range=0.2,
width_shift_range=0.2,height_shift_range=0.2,shear_range=0.1,fill_mode="nearest") | _____no_output_____ | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Calling Resnet50V2 Model | from tensorflow.keras.applications import ResNet50V2
resnet50v2 = tf.keras.applications.DenseNet201(input_shape=(100,100,3),include_top=False,weights='imagenet',pooling='avg')
resnet50v2.trainable = False | _____no_output_____ | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Making Deep CNN Model | model_input = resnet50v2.input
classifier = tf.keras.layers.Dense(128, activation='relu')(resnet50v2.output)
classifier = tf.keras.layers.Dense(64, activation='relu')(resnet50v2.output)
classifier = tf.keras.layers.Dense(512, activation='relu')(resnet50v2.output)
classifier = tf.keras.layers.Dense(128, activation='relu... | _____no_output_____ | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Compiling with ADAM Optimizer and Binary Crossentropy Loss Function | model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy']) | _____no_output_____ | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Fitting the Dataset into Model | history=model.fit(generator.flow(X_train,y_train,batch_size=32),validation_data=(X_test,y_test),epochs=50) | Epoch 1/50
5/5 [==============================] - 19s 1s/step - loss: 0.8226 - accuracy: 0.4309 - val_loss: 0.5457 - val_accuracy: 0.5556
Epoch 2/50
5/5 [==============================] - 1s 99ms/step - loss: 0.4008 - accuracy: 0.8104 - val_loss: 0.3669 - val_accuracy: 0.6667
Epoch 3/50
5/5 [===========================... | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Prediction on Test Set | y_pred=model.predict(X_test)
y_pred=np.argmax(y_pred,axis=1)
y_test = np.argmax(y_test,axis=1) | _____no_output_____ | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Confusion Matrix | from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test,y_pred)
print(cm)
plt.subplots(figsize=(15,7))
sns.heatmap(cm, annot= True, linewidth=1, cmap="autumn_r") | _____no_output_____ | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Accuracy | print("Accuracy : ",accuracy_score(y_test,y_pred)) | Accuracy : 0.9444444444444444
| MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Classification Report | print(classification_report(y_test,y_pred)) | precision recall f1-score support
0 1.00 1.00 1.00 10
1 0.94 0.94 0.94 16
2 0.90 0.90 0.90 10
accuracy 0.94 36
macro avg 0.95 0.95 0.95 ... | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Loss vs Validation Loss Plot | import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(y=history.history['loss'], name='Loss',
line=dict(color='royalblue', width=3)))
fig.add_trace(go.Scatter(y=history.history['val_loss'], name='Validation Loss',
line=dict(color='firebrick', wid... | _____no_output_____ | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Accuracy vs Validation Accuracy Plot | fig = go.Figure()
fig.add_trace(go.Scatter(y=history.history['accuracy'], name='Accuracy',
line=dict(color='royalblue', width=3)))
fig.add_trace(go.Scatter(y=history.history['val_accuracy'], name='Validation Accuracy',
line=dict(color='firebrick', width=3))) | _____no_output_____ | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Testing on some Random Images | image=load_img("/content/drive/MyDrive/rice/tungro/IMG_0852.jpg",target_size=(100,100))
imagee=load_img("/content/drive/MyDrive/rice/blight/IMG_0936.jpg",target_size=(100,100))
imageee=load_img("/content/drive/MyDrive/rice/blast/IMG_0560.jpg",target_size=(100,100))
imageeee=load_img("/content/drive/MyDrive/rice/blight/... | This Rice Belongs to tungro class
This Rice Belongs to blight class
This Rice Belongs to blast class
This Rice Belongs to blight class
This Rice Belongs to tungro class
| MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Prediction on Different Test Set | print(test.shape)
predictionn=model.predict(test)
print(predictionn.shape)
test_pred=[]
for item in predictionn:
value=np.argmax(item)
test_pred = test_pred + [value] | _____no_output_____ | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Confusion Matrix | from sklearn.metrics import confusion_matrix
cm = confusion_matrix(test_test,test_pred)
print(cm)
plt.subplots(figsize=(15,7))
sns.heatmap(cm, annot= True, linewidth=1, cmap="CMRmap") | _____no_output_____ | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Accuracy | accuracy=accuracy_score(test_test,test_pred)
print("Model Accuracy : ",accuracy) | Model Accuracy : 0.9706666666666667
| MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
Classification Report | print(classification_report(test_test,test_pred)) | precision recall f1-score support
0 0.95 0.95 0.95 20
1 0.95 1.00 0.98 20
2 1.00 0.95 0.97 20
accuracy 0.97 60
macro avg 0.97 0.97 0.97 ... | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture |
This Model can Successfully Detects The Disease of a Rice Leaf with an Accuracy of 97% | _____no_output_____ | MIT | rice_leaf_disease_detection_with_resnet50v2.ipynb | Chandramouli-Das/Rice-Leaf-Disease-Detection-using-ResNet50V2-Architecture | |
Stock Prediction using fb ProphetProphet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. ... | import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
from alpha_vantage.timeseries import TimeSeries
from fbprophet import Prophet
os.chdir(r'N:\STOCK ADVISOR BOT')
ALPHA_VANTAGE_API_KEY = 'XAGC5LBB1SI9RDLW'
ts = TimeSeries(key= ALPHA_VANTAGE_API_KEY, output_format='pandas')
df_Stock, Stoc... | _____no_output_____ | MIT | Stock Prediction/.ipynb_checkpoints/ADS_Stock_Prediction_Prophet_MSFT-checkpoint.ipynb | Chowry000/Stock-Prediction-Using-LSTM |
If you want to visualize the individual forecast components, we can use Prophet’s built-in plot_components method like below | prophet_model.plot_components(forcast);
forcast.shape
forcast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail() | _____no_output_____ | MIT | Stock Prediction/.ipynb_checkpoints/ADS_Stock_Prediction_Prophet_MSFT-checkpoint.ipynb | Chowry000/Stock-Prediction-Using-LSTM |
Prediction PerformanceThe performance_metrics utility can be used to compute some useful statistics of the prediction performance (yhat, yhat_lower, and yhat_upper compared to y), as a function of the distance from the cutoff (how far into the future the prediction was). The statistics computed are mean squared error ... | from fbprophet.diagnostics import cross_validation, performance_metrics
df_cv = cross_validation(prophet_model, horizon='180 days')
df_cv.head()
df_cv
df_p = performance_metrics(df_cv)
df_p.head()
df_p
from fbprophet.plot import plot_cross_validation_metric
fig = plot_cross_validation_metric(df_cv, metric='mape') | _____no_output_____ | MIT | Stock Prediction/.ipynb_checkpoints/ADS_Stock_Prediction_Prophet_MSFT-checkpoint.ipynb | Chowry000/Stock-Prediction-Using-LSTM |
PROJECT 1 Pseudocode For Project 1 AVOIDS INPUT input word and letters IF letters or words inputed are not forbidden RETURN "TRUE" END | def avoids():
forbidden = list(str(input("Input forbidden letters:")))
sentence = str(input("Input a sentence to be inspected:")).split("")
li =[]
no = 0
for i in sentence:
boo = True
for j in forbidden:
if j in i:
boo = False
if boo == False:
... | Input forbidden letters:rtre
Input a sentence to be inspected: oj is a GOAT
| MIT | WEEK5/WEEK5_PROJECTS.ipynb | ayomideoj/ayomideojikutuCSC102 |
PROJECT 2 Pseudocode for Project 2 USES ALL INPUT word and strings of letters IF word uses all required letters at least once RETURN "TRUE" ELSE "FALSE" END | def uses_all (word, req):
newReq = list(req)
li = []
for i in newReq:
if i in word:
continue
else:
li.append(i)
if len(li) > 0:
print(False)
else:
print(True)
print("the required letters",li,"not found",word)
uses_all("elegaont","eguon... | False
the required letters ['u'] not found elegaont
| MIT | WEEK5/WEEK5_PROJECTS.ipynb | ayomideoj/ayomideojikutuCSC102 |
PROJECT 3 Pseudocode for Project 3 INPUT Jane = "odd" INPUT Jack = "even" IF Jack + Jane = odd number PRINT "Jane wins" ELIF Jack + Jane = even number PRINT "Jack wins" END | import math
a = str(input("Player1, enter guess"))
b = str(input("Player2, enter guess"))
num1 = int(input("Player1, what number did you choose"))
num2 = int(input("Player2, what number did you choose"))
total = num1 + num2
if total % 2 == 0:
nature = "even"
else:
nature = "odds"
if a == nature and b == natu... | Player1, enter guess3
Player2, enter guess4
Player1, what number did you choose3
Player2, what number did you choose4
Nobody wins
| MIT | WEEK5/WEEK5_PROJECTS.ipynb | ayomideoj/ayomideojikutuCSC102 |
PROJECT 4 Pseudocode for Project 4 Velocity, V = D/T Distance, D = 50 Time, T = 2 CALCULATE V END | d = int(input("Enter value of distance"))
t = int(input("Enter value of time"))
v= d/t
print("the care is moving at",v,"miles per hour") | Enter value of distance400
Enter value of time2
the care is moving at 200.0 miles per hour
| MIT | WEEK5/WEEK5_PROJECTS.ipynb | ayomideoj/ayomideojikutuCSC102 |
Linear Models: Multiple variables with interactions Contents1 Introduction1.1 Chapter aims1.2 Formulae with interactions in R2 Model 1: Mammalian genome size3 Model 2 (ANCOVA): Body Weight in Odonata Introduction Here you will build on your skills in fitting l... | load('../data/mammals.Rdata') | _____no_output_____ | MIT | notebooks/17-MulExplInter.ipynb | mathemage/TheMulQuaBio |
If `mammals.Rdata` is missing, just import the data again using `read.csv`. You will then have to add the log C Value column to the imported data frame again.Let's refit the model from [the first multiple explanatory variables chapter](16-MulExpl.ipynb), but including the interaction between trophic level and ground dw... | model <- lm(logCvalue ~ TrophicLevel * GroundDwelling, data= mammals)
par(mfrow=c(2,2), mar=c(3,3,1,1), mgp=c(2, 0.8,0))
plot(model) | _____no_output_____ | MIT | notebooks/17-MulExplInter.ipynb | mathemage/TheMulQuaBio |
Now, examine the `anova` and `summary` outputs for the model: | anova(model) | _____no_output_____ | MIT | notebooks/17-MulExplInter.ipynb | mathemage/TheMulQuaBio |
Compared to the model from [the first multiple explanatory variables chapter](16-MulExpl.ipynb), there is an extra line at the bottom. The top two are the same and show that trophic level and ground dwelling both have independent main effects. The extra lineshows that there is also an interaction between the two. It do... | summary(model) | _____no_output_____ | MIT | notebooks/17-MulExplInter.ipynb | mathemage/TheMulQuaBio |
The lines in this output are:1. The reference level (intercept) for non ground dwelling carnivores. (The reference level is decided just by the alphabetic order of the levels)2. Two differences for being in different trophic levels.3. One difference for being ground dwelling4. Two new differences that give specific dif... | # a data frame of combinations of variables
gd <- rep(levels(mammals$GroundDwelling), times = 3)
print(gd)
tl <- rep(levels(mammals$TrophicLevel), each = 2)
print(tl)
# New data frame
predVals <- data.frame(GroundDwelling = gd, TrophicLevel = tl)
# predict using the new data frame
predVals$predict <- predict(model, ne... | GroundDwelling TrophicLevel predict
1 No Carnivore 0.9589465
2 Yes Carnivore 1.2138170
3 No Herbivore 1.0124594
4 Yes Herbivore 1.2976624
5 No Omnivore 1.1917603
6 Yes Omnivore 1.2990165
| MIT | notebooks/17-MulExplInter.ipynb | mathemage/TheMulQuaBio |
$\star$ Include and run the code for gererating these predictions in your script.If we plot these data points onto the barplot from [the first multiple explanatory variables chapter](16-MulExpl.ipynb), they now lie exactly on the mean values, because we've allowed for interactions. The triangle on this plot shows the p... | odonata <- read.csv('../data/GenomeSize.csv') | _____no_output_____ | MIT | notebooks/17-MulExplInter.ipynb | mathemage/TheMulQuaBio |
$\star$ Now create two new variables in the `odonata` data set called `logGS` and `logBW` containing log genome size and log body weight: | odonata$logGS <- log(odonata$GenomeSize)
odonata$logBW <- log(odonata$BodyWeight) | _____no_output_____ | MIT | notebooks/17-MulExplInter.ipynb | mathemage/TheMulQuaBio |
The models we fitted [before](12-ExpDesign.ipynb) looked like this: We can now fit the model of body weight as a function of both genome size and suborder: | odonModel <- lm(logBW ~ logGS * Suborder, data = odonata) | _____no_output_____ | MIT | notebooks/17-MulExplInter.ipynb | mathemage/TheMulQuaBio |
Again, we'll look at the anova table first: | anova(odonModel) | _____no_output_____ | MIT | notebooks/17-MulExplInter.ipynb | mathemage/TheMulQuaBio |
Interpreting this:* There is no significant main effect of log genome size. The *main* effect is the important thing here — genome size is hugely important but does very different things for the two different suborders. If we ignored `Suborder`, there isn't an overall relationship: the average of those two lines is pre... | summary(odonModel) | _____no_output_____ | MIT | notebooks/17-MulExplInter.ipynb | mathemage/TheMulQuaBio |
* The first thing to note is that the $r^2$ value is really high. The model explains three quarters (0.752) of the variation in the data.* Next, there are four coefficients: * The intercept is for the first level of `Suborder`, which is Anisoptera (dragonflies). * The next line, for `log genome size`, is the slop... | #get the range of the data:
rng <- range(odonata$logGS)
#get a sequence from the min to the max with 100 equally spaced values:
LogGSForFitting <- seq(rng[1], rng[2], length = 100) | _____no_output_____ | MIT | notebooks/17-MulExplInter.ipynb | mathemage/TheMulQuaBio |
Have a look at these numbers: | print(LogGSForFitting) | [1] -0.891598119 -0.873918728 -0.856239337 -0.838559945 -0.820880554
[6] -0.803201163 -0.785521772 -0.767842380 -0.750162989 -0.732483598
[11] -0.714804206 -0.697124815 -0.679445424 -0.661766032 -0.644086641
[16] -0.626407250 -0.608727859 -0.591048467 -0.573369076 -0.555689685
[21] -0.538010293 -0.520330902 -0.5... | MIT | notebooks/17-MulExplInter.ipynb | mathemage/TheMulQuaBio |
We can now use the model to predict the values of body weight at each of those points for each of the two suborders: | #get a data frame of new data for the order
ZygoVals <- data.frame(logGS = LogGSForFitting, Suborder = "Zygoptera")
#get the predictions and standard error
ZygoPred <- predict(odonModel, newdata = ZygoVals, se.fit = TRUE)
#repeat for anisoptera
AnisoVals <- data.frame(logGS = LogGSForFitting, Suborder = "Anisoptera")... | _____no_output_____ | MIT | notebooks/17-MulExplInter.ipynb | mathemage/TheMulQuaBio |
We've added `se.fit=TRUE` to the function to get the standard error around the regression lines. Both `AnisoPred` and `ZygoPred` contain predicted values (called `fit`) and standard error values (called `se.fit`) for each of the values in our generated values in `LogGSForFitting` for each of the two suborders.We can ad... | # plot the scatterplot of the data
plot(logBW ~ logGS, data = odonata, col = Suborder)
# add the predicted lines
lines(AnisoPred$fit ~ LogGSForFitting, col = "black")
lines(AnisoPred$fit + AnisoPred$se.fit ~ LogGSForFitting, col = "black", lty = 2)
lines(AnisoPred$fit - AnisoPred$se.fit ~ LogGSForFitting, col = "black"... | _____no_output_____ | MIT | notebooks/17-MulExplInter.ipynb | mathemage/TheMulQuaBio |
Optimization MethodsUntil now, you've always used Gradient Descent to update the parameters and minimize the cost. In this notebook, you will learn more advanced optimization methods that can speed up learning and perhaps even get you to a better final value for the cost function. Having a good optimization algorithm ... | import numpy as np
import matplotlib.pyplot as plt
import scipy.io
import math
import sklearn
import sklearn.datasets
from opt_utils import load_params_and_grads, initialize_parameters, forward_propagation, backward_propagation
from opt_utils import compute_cost, predict, predict_dec, plot_decision_boundary, load_data... | _____no_output_____ | Apache-2.0 | Week2/Optimization+methods.ipynb | softwarebrahma/Deep-Learning-Specialization-Improving-Deep-Neural-Network-Hyperparam-TuneRegularizationOptimization |
1 - Gradient DescentA simple optimization method in machine learning is gradient descent (GD). When you take gradient steps with respect to all $m$ examples on each step, it is also called Batch Gradient Descent. **Warm-up exercise**: Implement the gradient descent update rule. The gradient descent rule is, for $l = ... | # GRADED FUNCTION: update_parameters_with_gd
def update_parameters_with_gd(parameters, grads, learning_rate):
"""
Update parameters using one step of gradient descent
Arguments:
parameters -- python dictionary containing your parameters to be updated:
parameters['W' + str(l)] =... | W1 = [[ 1.63535156 -0.62320365 -0.53718766]
[-1.07799357 0.85639907 -2.29470142]]
b1 = [[ 1.74604067]
[-0.75184921]]
W2 = [[ 0.32171798 -0.25467393 1.46902454]
[-2.05617317 -0.31554548 -0.3756023 ]
[ 1.1404819 -1.09976462 -0.1612551 ]]
b2 = [[-0.88020257]
[ 0.02561572]
[ 0.57539477]]
| Apache-2.0 | Week2/Optimization+methods.ipynb | softwarebrahma/Deep-Learning-Specialization-Improving-Deep-Neural-Network-Hyperparam-TuneRegularizationOptimization |
**Expected Output**: **W1** [[ 1.63535156 -0.62320365 -0.53718766] [-1.07799357 0.85639907 -2.29470142]] **b1** [[ 1.74604067] [-0.75184921]] **W2** [[ 0.32171798 -0.25467393 1.46902454] [-2.05617317 -0.31554548 -0.3756023 ] [ 1.140... | # GRADED FUNCTION: random_mini_batches
def random_mini_batches(X, Y, mini_batch_size = 64, seed = 0):
"""
Creates a list of random minibatches from (X, Y)
Arguments:
X -- input data, of shape (input size, number of examples)
Y -- true "label" vector (1 for blue dot / 0 for red dot), of shape (... | shape of the 1st mini_batch_X: (12288, 64)
shape of the 2nd mini_batch_X: (12288, 64)
shape of the 3rd mini_batch_X: (12288, 20)
shape of the 1st mini_batch_Y: (1, 64)
shape of the 2nd mini_batch_Y: (1, 64)
shape of the 3rd mini_batch_Y: (1, 20)
mini batch sanity check: [ 0.90085595 -0.7612069 0.2344157 ]
| Apache-2.0 | Week2/Optimization+methods.ipynb | softwarebrahma/Deep-Learning-Specialization-Improving-Deep-Neural-Network-Hyperparam-TuneRegularizationOptimization |
**Expected Output**: **shape of the 1st mini_batch_X** (12288, 64) **shape of the 2nd mini_batch_X** (12288, 64) **shape of the 3rd mini_batch_X** (12288, 20) **shape of the 1st mini_batch_Y** (1, 64) ... | # GRADED FUNCTION: initialize_velocity
def initialize_velocity(parameters):
"""
Initializes the velocity as a python dictionary with:
- keys: "dW1", "db1", ..., "dWL", "dbL"
- values: numpy arrays of zeros of the same shape as the corresponding gradients/parameters.
Argumen... | v["dW1"] = [[ 0. 0. 0.]
[ 0. 0. 0.]]
v["db1"] = [[ 0.]
[ 0.]]
v["dW2"] = [[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
v["db2"] = [[ 0.]
[ 0.]
[ 0.]]
| Apache-2.0 | Week2/Optimization+methods.ipynb | softwarebrahma/Deep-Learning-Specialization-Improving-Deep-Neural-Network-Hyperparam-TuneRegularizationOptimization |
**Expected Output**: **v["dW1"]** [[ 0. 0. 0.] [ 0. 0. 0.]] **v["db1"]** [[ 0.] [ 0.]] **v["dW2"]** [[ 0. 0. 0.] [ 0. 0. 0.] [ 0. 0. 0.]] **v["db2"]** [[ 0.] [ 0.] [ 0.]] **Exercise**: ... | # GRADED FUNCTION: update_parameters_with_momentum
def update_parameters_with_momentum(parameters, grads, v, beta, learning_rate):
"""
Update parameters using Momentum
Arguments:
parameters -- python dictionary containing your parameters:
parameters['W' +
s... | W1 = [[ 1.62544598 -0.61290114 -0.52907334]
[-1.07347112 0.86450677 -2.30085497]]
b1 = [[ 1.74493465]
[-0.76027113]]
W2 = [[ 0.31930698 -0.24990073 1.4627996 ]
[-2.05974396 -0.32173003 -0.38320915]
[ 1.13444069 -1.0998786 -0.1713109 ]]
b2 = [[-0.87809283]
[ 0.04055394]
[ 0.58207317]]
v["dW1"] = [[-0.11006192 ... | Apache-2.0 | Week2/Optimization+methods.ipynb | softwarebrahma/Deep-Learning-Specialization-Improving-Deep-Neural-Network-Hyperparam-TuneRegularizationOptimization |
**Expected Output**: **W1** [[ 1.62544598 -0.61290114 -0.52907334] [-1.07347112 0.86450677 -2.30085497]] **b1** [[ 1.74493465] [-0.76027113]] **W2** [[ 0.31930698 -0.24990073 1.4627996 ] [-2.05974396 -0.32173003 -0.38320915] [ 1.134... | # GRADED FUNCTION: initialize_adam
def initialize_adam(parameters) :
"""
Initializes v and s as two python dictionaries with:
- keys: "dW1", "db1", ..., "dWL", "dbL"
- values: numpy arrays of zeros of the same shape as the corresponding gradients/parameters.
Arguments:... | v["dW1"] = [[ 0. 0. 0.]
[ 0. 0. 0.]]
v["db1"] = [[ 0.]
[ 0.]]
v["dW2"] = [[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
v["db2"] = [[ 0.]
[ 0.]
[ 0.]]
s["dW1"] = [[ 0. 0. 0.]
[ 0. 0. 0.]]
s["db1"] = [[ 0.]
[ 0.]]
s["dW2"] = [[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
s["db2"] = [[ 0.]
[ 0.]
[ 0.]]
| Apache-2.0 | Week2/Optimization+methods.ipynb | softwarebrahma/Deep-Learning-Specialization-Improving-Deep-Neural-Network-Hyperparam-TuneRegularizationOptimization |
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