  Detecting large-scale flux ropes (FRs) embedded in interplanetary coronal
mass ejections (ICMEs) and assessing their geoeffectiveness are essential since
they can drive severe space weather. At 1 au, these FRs have an average
duration of 1 day. Their most common magnetic features are large, smoothly
rotating magnetic fields. Their manual detection has become a relatively common
practice over decades, although visual detection can be time-consuming and
subject to observer bias. Our study proposes a pipeline that utilizes two
supervised binary-classification machine learning (ML) models trained with
solar wind magnetic properties to automatically detect large-scale FRs and
additionally determine their geoeffectiveness. The first model is used to
generate a list of auto-detected FRs. Using the properties of southward
magnetic field the second model determines the geoeffectiveness of FRs. Our
method identifies 88.6\% and 80\% large-scale ICMEs (duration $\ge 1$ day)
observed at 1 au by Wind and Sun Earth Connection Coronal and Heliospheric
Investigation (STEREO) mission, respectively. While testing with a continuous
solar wind data obtained from Wind, our pipeline detected 56 of the 64
large-scale ICMEs during 2008 - 2014 period (recall= 0.875) but many false
positives (precision= 0.56) as we do not take into account any additional solar
wind properties than the magnetic properties. We found an accuracy of 0.88 when
estimating the geoeffectiveness of the auto-detected FRs using our method.
Thus, in space weather now-casting and forecasting at L1 or any planetary
missions, our pipeline can be utilized to offer a first-order detection of
large-scale FRs and geoeffectiveness.
