def feature_importance_plot(df,target):
    from sklearn.ensemble import RandomForestClassifier
    model = RandomForestClassifier()
    y = df[target]
    x = df.drop(target,axis=1)
    model.fit(x,y)
    res_df = pd.DataFrame({'feature':x.columns,'importance':model.feature_importances_})
    res = res_df.sort_values('importance',ascending=False)
    res['cum_importance'] = res.importance.cumsum()
    plt.subplot(211)
    sns.barplot(res.feature,res.importance)
    plt.subplot(212)
    plt.plot(np.arange(1,res.shape[0]+1),res.cum_importance,linewidth=2)
    return(res)