# Prediction, Classification

Estimated reading time: 15 minutesWelcome! This section highlights important business machine learning models. Many of these models are not code-complete and simply provide excerpted pseudo-like code. The code on this website is in the Python programming language.

This six-part documentation identifies:

- State of the art classification models (this page).
- Continuous value prediction problems
- The use of Natural Language Processing
- Important time series solutions
- The core principles of recommender systems
- And experimental image and voice technologies

This section is more easily explorable with some knowledge of Python; especially of its data science components. If you need any help with the models feel free to get in touch for a consultation.

### Binary Classification

A lot of classification problems are binary in nature such as predicting whether the stock price will go up or down in the future, predicting gender and predicting wether a prospective client will buy your product.

##### Binary Business Prediction:

- Future direction of commodity, stocks and bonds prices.
- Predicting a customer demographic.
- Predict wheteher customers will respond to direct mail.
- Predict the pobabilitiy of damage in a home inspection
- Predict the likelihood that a grant application will succeed.
- Predict job success using a 10 part questionaire.
- Predict those most likley to donate to a cause.

Data Types | Description | Description |
---|---|---|

Categorical | Data that can be discretely classified. | Country, Exchange, Currency, Dummy Variable, State, Industry. |

Continuous | Data that incrementally changes in values | Past Asset Price, Interest Rate, Competitors Price. |

Stepped | Similar to continuos but changes infrequently | P/E, Quarterly Revenue, |

Transformed Category | A different datatype converted to categorical. | Traded inside standard deviation - yes, no. P/E above 10 - yes, no. |

Models | The prediction of additional models | ARIMA, AR, MA. |

## premodel

```
#Load Data:
import pandas as pd
train = pd.read_csv("../input/train_1.csv")
#Explore For Insights:
import matplotlib.pyplot as plt
plt.plot(mean_group)
plt.show()
#Split Data in Three Sets:
from sklearn.model_selection import train_test_split
```

```
X_holdout = X.iloc[:int(len(X),:]
X_rest = X[X[~X_holdout]]
y_holdout = y.iloc[:int(len(y),:]
y_rest = y[y[~y_holdout]]
X_train, X_test, y_train, y_test = train_test_split(X_rest, y, test_size = 0.3, random_state = 0)
#Add Additional Features:
mean = X_train[col].mean()
```

## model

```
import lightgbm as lgbm
learning_rate = 0.8
num_leaves =128
min_data_in_leaf = 1000
feature_fraction = 0.5
bagging_freq=1000
num_boost_round = 1000
params = {"objective": "binary",
"boosting_type": "gbdt",
"learning_rate": learning_rate,
"num_leaves": num_leaves,
"feature_fraction": feature_fraction,
"bagging_freq": bagging_freq,
"verbosity": 0,
"metric": "binary_logloss",
"nthread": 4,
"subsample": 0.9
}
dtrain = lgbm.Dataset(X_train, y_train)
dvalid = lgbm.Dataset(X_validate, y_test, reference=dtrain)
bst = lgbm.train(params, dtrain, num_boost_round, valid_sets=dvalid, verbose_eval=100,early_stopping_rounds=100)
bst.predict(X_test, num_iteration=bst.best_iteration)
```

```
import xgboost as XGB
model = xgb.XGBClassifier(objective='binary:logistic',
learning_rate=0.037, max_depth=5,
min_child_weight=20, n_estimators=180,
reg_lambda=0.8,booster = 'gbtree',
subsample=0.9, silent=1,
nthread = -1)
model.fit(train[feature_names], target)
pred = model.predict(test[feature_names])
```

## postmodel

```
#Predict:
y_pred = regressor.predict(X_test)
y_pred = sc.inverse_transform(y_pred)
#Assess Success of Prediction:
ROC AUC
TP/TN
F1
Confusion Matrix
#Tweak Parameters to Optimise Metrics:
#Select A new Model
#Repeat the process.
#Final Showdown
Measure the performance of all models against the holdout set.
And pick the final model.
```

## premodel

```
#Load Data:
import pandas as pd
train = pd.read_csv("../input/train_1.csv")
#Explore For Insights:
import matplotlib.pyplot as plt
plt.plot(mean_group)
plt.show()
#Split Data in Three Sets:
from sklearn.model_selection import train_test_split
```

```
X_holdout = X.iloc[:int(len(X),:]
X_rest = X[X[~X_holdout]]
y_holdout = y.iloc[:int(len(y),:]
y_rest = y[y[~y_holdout]]
X_train, X_test, y_train, y_test = train_test_split(X_rest, y, test_size = 0.3, random_state = 0)
#Add Additional Features:
mean = X_train[col].mean()
```

## model

```
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.optimizers import SGD
from keras.models import Sequential
from keras.layers import Dense, Flatten
def create_model():
conv = Sequential()
conv.add(Conv1D(20, 4, input_shape = PRED.shape[1:3], activation = 'relu'))
conv.add(MaxPooling1D(2))
conv.add(Dense(50, activation='relu'))
conv.add(Flatten())
conv.add(Dense(1, activation = 'sigmoid'))
sgd = SGD(lr = 0.1, momentum = 0.9, decay = 0, nesterov = False)
conv.compile(loss = 'binary_crossentropy', optimizer = sgd, metrics = ['accuracy'])
return conv
model = KerasClassifier(build_fn=create_model, batch_size = 500, epochs = 20, verbose = 1,class_weight=class_weight)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
```

## postmodel

```
#Predict:
y_pred = regressor.predict(X_test)
y_pred = sc.inverse_transform(y_pred)
#Assess Success of Prediction:
ROC AUC
TP/TN
F1
Confusion Matrix
#Tweak Parameters to Optimise Metrics:
#Select A new Model
#Repeat the process.
#Final Showdown
Measure the performance of all models against the holdout set.
And pick the final model.
```

## premodel

```
#Load Data:
import pandas as pd
train = pd.read_csv("../input/train_1.csv")
#Explore For Insights:
import matplotlib.pyplot as plt
plt.plot(mean_group)
plt.show()
#Split Data in Three Sets:
from sklearn.model_selection import train_test_split
X_holdout = X.iloc[:int(len(X),:]
X_rest = X[X[~X_holdout]]
y_holdout = y.iloc[:int(len(y),:]
y_rest = y[y[~y_holdout]]
X_train, X_test, y_train, y_test = train_test_split(X_rest, y, test_size = 0.3, random_state = 0)
#Add Additional Features:
mean = X_train[col].mean()
```

## model

```
def create_baseline():
# create model
model = Sequential()
model.add(Dense(10, input_dim=30, kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))
# Compile model. We use the the logarithmic loss function, and the Adam gradient optimizer.
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
model = KerasClassifier(build_fn=create_baseline, epochs=100, batch_size=5, verbose=0)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
```

## postmodel

```
#Predict:
y_pred = regressor.predict(X_test)
y_pred = sc.inverse_transform(y_pred)
#Assess Success of Prediction:
ROC AUC
TP/TN
F1
Confusion Matrix
#Tweak Parameters to Optimise Metrics:
#Select A new Model
#Repeat the process.
#Final Showdown
Measure the performance of all models against the holdout set.
And pick the final model.
```

### Multi-class Classification

This section relates to predictions for multiple classes. Machine learning has significantly enahnced the quality and accuracy of multiple class/lable predictions.

#### Multi-class Prediction

- Item specific sales prediction i.e. unit of sales.
- Predicting store sales.
- Predict the unit of sales from multiple items.
- Predicting the likelihood of certain crimes occuring at different points geographically and at different times.
- What when, where and at what severity will the flu strike.
- New empoyees predict the level of access and what access they require.
- Predict the most pressing community issue.
- What customers wil purchase what policy.
- Predict which shoppers are most likely to repeat purchase.
- Predict which blog post from a selection would be most popular
- Predict destination of taxi with initial partial trajectories.

Data Types | Description | Description |
---|---|---|

Categorical | Data that can be discretely classified. | Country, Exchange, Currency, Dummy Variable, State, Industry. |

Continuous | Data that incrementally changes in values | Past Asset Price, Interest Rate, Competitors Price. |

Stepped | Similar to continuos but changes infrequently | P/E, Quarterly Revenue, |

Transformed Category | A different datatype converted to categorical. | Traded inside standard deviation - yes, no. P/E above 10 - yes, no. |

Models | The prediction of additional models | ARIMA, AR, MA. |

## premodel

```
#Load Data:
import pandas as pd
train = pd.read_csv("../input/train_1.csv")
#Explore For Insights:
import matplotlib.pyplot as plt
plt.plot(mean_group)
plt.show()
#Split Data in Three Sets:
from sklearn.model_selection import train_test_split
X_holdout = X.iloc[:int(len(X),:]
X_rest = X[X[~X_holdout]]
y_holdout = y.iloc[:int(len(y),:]
y_rest = y[y[~y_holdout]]
X_train, X_test, y_train, y_test = train_test_split(X_rest, y, test_size = 0.3, random_state = 0)
#Add Additional Features:
mean = X_train[col].mean()
```

## model

```
import lightgbm as lgbm
learning_rate = 0.8
num_leaves =128
min_data_in_leaf = 1000
feature_fraction = 0.5
bagging_freq=1000
num_boost_round = 1000
params = {"objective": "multiclass",
"boosting_type": "gbdt",
"learning_rate": learning_rate,
"num_leaves": num_leaves,
"feature_fraction": feature_fraction,
"bagging_freq": bagging_freq,
"verbosity": 0,
"metric": "multi_logloss",
"nthread": 4,
"subsample": 0.9
}
dtrain = lgbm.Dataset(X_train, y_train)
dvalid = lgbm.Dataset(X_validate, y_test, reference=dtrain)
bst = lgbm.train(params, dtrain, num_boost_round, valid_sets=dvalid, verbose_eval=100,early_stopping_rounds=100)
bst.predict(X_test, num_iteration=bst.best_iteration)
```

```
import xgboost as XGB
model = xgb.XGBClassifier(objective='multi:softmax',
learning_rate=0.037, max_depth=5,
min_child_weight=20, n_estimators=180,
reg_lambda=0.8,booster = 'gbtree',
subsample=0.9, silent=1,
nthread = -1)
model.fit(train[feature_names], target)
pred = model.predict(test[feature_names])
```

## postmodel

```
#Predict:
y_pred = regressor.predict(X_test)
y_pred = sc.inverse_transform(y_pred)
#Assess Success of Prediction:
ROC AUC
TP/TN
F1
Confusion Matrix
#Tweak Parameters to Optimise Metrics:
#Select A new Model
#Repeat the process.
#Final Showdown
Measure the performance of all models against the holdout set.
And pick the final model.
```

## premodel

```
#Load Data:
import pandas as pd
train = pd.read_csv("../input/train_1.csv")
#Explore For Insights:
import matplotlib.pyplot as plt
plt.plot(mean_group)
plt.show()
#Split Data in Three Sets:
from sklearn.model_selection import train_test_split
X_holdout = X.iloc[:int(len(X),:]
X_rest = X[X[~X_holdout]]
y_holdout = y.iloc[:int(len(y),:]
y_rest = y[y[~y_holdout]]
X_train, X_test, y_train, y_test = train_test_split(X_rest, y, test_size = 0.3, random_state = 0)
#Add Additional Features:
mean = X_train[col].mean()
```

## model

```
def create_baseline():
# create model
model = Sequential()
model.add(Dense(10, input_dim=30, kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))
# Compile model. We use the the logarithmic loss function, and the Adam gradient optimizer.
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
model = KerasClassifier(build_fn=create_baseline, epochs=100, batch_size=5, verbose=0)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
```

## postmodel

```
#Predict:
y_pred = regressor.predict(X_test)
y_pred = sc.inverse_transform(y_pred)
#Assess Success of Prediction:
ROC AUC
TP/TN
F1
Confusion Matrix
#Tweak Parameters to Optimise Metrics:
#Select A new Model
#Repeat the process.
#Final Showdown
Measure the performance of all models against the holdout set.
And pick the final model.
```

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