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diff --git a/tests/baseline_images/test_regressor/test_effect/test_quick_method.png b/tests/baseline_images/test_regressor/test_effect/test_quick_method.png
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diff --git a/tests/test_regressor/test_effect.py b/tests/test_regressor/test_effect.py
new file mode 100644
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+++ b/tests/test_regressor/test_effect.py
@@ -0,0 +1,54 @@
+"""
+Test the EffectPlot functionality
+"""
+
+##########################################################################
+## Imports
+##########################################################################
+
+import pytest
+
+from yellowbrick.regressor.effect import *
+from yellowbrick.datasets import load_concrete
+
+from tests.base import VisualTestCase
+
+from sklearn.linear_model import LinearRegression, Lasso, Ridge
+
+##########################################################################
+## EffectPlot Tests
+##########################################################################
+
+class TestEffectPlot(VisualTestCase):
+    """
+    Test the EffectPlot base class
+    """
+
+    def test_quick_method(self):
+        """
+        Test quick method
+        """
+        X, y = load_concrete()
+        visualizer = effectplot(LinearRegression(), X, y, show=False)
+        self.assert_images_similar(visualizer)
+    
+    def test_marker(self):
+        """
+        Test circular marker.
+        """
+        X, y = load_concrete()
+        visualizer = EffectPlot(LinearRegression(), marker='.', colormap='cool')
+        visualizer.fit(X, y)
+        visualizer.finalize()
+        self.assert_images_similar(visualizer)
+    
+    @pytest.mark.parametrize("model", [LinearRegression(), Lasso(alpha=100), Ridge()])
+    def test_other_linear_models(self, model):
+        """
+        Test other linear_models
+        """
+        message = "case failed for {}".format(model)
+        viz = EffectPlot(model=model, marker='.', colormap='cool')
+        assert viz.estimator == model, message
+            
+        
\ No newline at end of file
diff --git a/yellowbrick/regressor/effect.py b/yellowbrick/regressor/effect.py
new file mode 100644
index 000000000..b9a2316cc
--- /dev/null
+++ b/yellowbrick/regressor/effect.py
@@ -0,0 +1,164 @@
+import numpy as np
+import pandas as pd
+from .base import RegressionScoreVisualizer
+from yellowbrick.style.colors import resolve_colors
+from yellowbrick.features.base import DataVisualizer
+from yellowbrick.utils.target import TargetType
+
+
+import matplotlib.pyplot as plt
+
+##########################################################################
+## Effect Plots
+##########################################################################
+
+class EffectPlot(RegressionScoreVisualizer):
+    """
+    Parameters
+    ----------
+  
+    model : a Scikit-Learn regressor
+        Should be an instance of a regressor, otherwise will raise a
+        YellowbrickTypeError exception on instantiation.
+        If the estimator is not fitted, it is fit when the visualizer is fitted,
+        unless otherwise specified by ``is_fitted``.
+
+    ax : matplotlib Axes, default: None
+        The axes to plot the figure on. If None is passed in the current axes.
+        will be used (or generated if required).
+
+    colors : list or tuple, default: None
+        A single color to plot all instances as or a list of colors to color each
+        instance according to its class in the discrete case or as an ordered
+        colormap in the sequential case. If not enough colors per class are
+        specified then the colors are treated as a cycle.
+
+    colormap : string or cmap, default: None
+        The colormap used to create the individual colors. In the discrete case
+        it is used to compute the number of colors needed for each class and
+        in the continuous case it is used to create a sequential color map based
+        on the range of the target.
+        
+    marker : string, default:'D'
+        Shape of the outliers in the boxplot. 'D' for diamond, '.' for 
+        circles
+
+    kwargs : dict
+        Keyword arguments that are passed to the base class and may influence
+        the visualization as defined in other Visualizers.
+    """
+    
+    def __init__(self,model,ax=None, colors=None, colormap=None, marker='D', **kwargs):
+        super(EffectPlot, self).__init__(model=model, ax=ax, **kwargs)
+        self.colors = colors
+        self.colormap = colormap
+        self.marker=marker
+        self.target_type = 'auto'
+    
+    def fit(self, X, y, **kwargs):
+        """
+        Fits the estimator to dataset and then multiply weights of a feature
+        with each feature values to generate new dataset.
+        Parameters
+        ----------
+        X : ndarray or DataFrame of shape n x m
+            A matrix of n instances with m features
+
+        y : ndarray or Series of length n
+            An array or series of target values
+
+        kwargs: keyword arguments passed to Scikit-Learn API.
+
+        Returns
+        -------
+        self : EffectPlot
+            The visualizer instance
+        """
+        categorical = self._get_categorical_columns(X)
+
+        #Encoding
+        dummy_df = pd.get_dummies(X, columns=categorical, drop_first=True)
+
+        self.estimator.fit(dummy_df, y)
+        coeffs = self.estimator.coef_
+        dummy_df = dummy_df*coeffs
+        
+        # Recollect the columns
+        for cols in categorical:
+            col_names = [a for a in dummy_df.columns.values if cols in a]
+            dummy_df[cols] = (dummy_df[col_names]).sum(axis=1)
+            dummy_df.drop(columns = col_names, inplace=True)
+              
+        self.draw(dummy_df)
+        self.columns = dummy_df.columns 
+        return self
+        
+    def _get_categorical_columns(self, X):
+        """
+        Finds the categorical features among the given features. Uses
+        _determine_targer_color_type from `DataVisualizer` for this purpose.
+        """
+        categorical=[]
+        cols = X.columns
+        for col in cols:
+            DataVisualizer._determine_target_color_type(self, X[col])
+            if(self._target_color_type==TargetType.DISCRETE):
+                categorical.append(col)
+        return categorical
+        
+        
+    def draw(self, X, **kwargs):
+        """
+        Draws a box plot. Provide control over almost anything in the plot.
+        """
+        colors = resolve_colors(
+                    n_colors=len(X.columns.values), 
+                    colormap=self.colormap, 
+                    colors=self.colors
+                    )
+        self.ax.vlines(x=0,ymin=0,ymax=X.shape[1]+1,linestyles='dashed', alpha=0.5)
+        X = np.array(X)
+        bp = self.ax.boxplot(X, sym='black', patch_artist=True,vert=False)
+        for (box,color) in zip(bp['boxes'],colors):
+            box.set( facecolor=color, linewidth=2)
+        for median in bp['medians']:
+            median.set(color='black', linewidth=3)
+        for whisker in bp['whiskers']:
+            whisker.set(color='black', linewidth=2)
+        for cap in bp['caps']:
+            cap.set(color='black', linewidth=2)
+        for flier in bp['fliers']:
+            # TODO: Set color of flier here
+            flier.set(marker=self.marker)
+        return self.ax
+
+        
+    def finalize(self):
+        self.ax.set_xlabel('Feature effect')
+        self.ax.set_yticklabels(self.columns)
+        self.ax.set_title('Effect Plot')
+        plt.tight_layout()
+        
+        
+##########################################################################
+## Quick Method
+##########################################################################
+        
+def effectplot(model, X, y, ax=None, colors=None, colormap=None, marker='D', 
+               show=True, **kwargs):
+    """
+    Quick Method.
+    """
+    viz = EffectPlot(model=model, ax=ax, colors=colors, colormap=colormap, 
+                            marker=marker, **kwargs)
+    viz.fit(X, y)
+       
+    if show:
+        viz.show()
+    else:
+        viz.finalize()
+    
+    return viz
+
+#Alias for Effectplot
+EffectViz = EffectPlot
\ No newline at end of file